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Research | american thesaurus.

moon over someone

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to spend time thinking about someone or something that you love, in a silly way that does not achieve anything

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Wordle Review No. 1,119, July 12, 2024

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Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena

• Haipeng Luo ,
• Qingfeng Sun ,
• Qingwei Lin 林庆维 ,
• Jianguang Lou ,
• Shifeng Chen ,
• Yansong Tang ,
• Weizhu Chen

Assessing the effectiveness of large language models (LLMs) presents significant challenges. The method of conducting human-annotated battles in an online Chatbot Arena has been recognized as a highly effective evaluative approach. However, this process is hindered by the costliness and time demands of human annotation, complicating the enhancement of LLMs via post-training. In this paper, we introduce ” Arena Learning ”, an innovative offline strategy designed to simulate these arena battles. We have developed a comprehensive set of instructions for simulated battles and employ AI-driven annotations to assess battle outcomes, facilitating continuous improvement of the target model through both supervised fine-tuning and reinforcement learning. A crucial aspect of our methodology is ensuring precise evaluations and achieving consistency between offline simulations and online competitions. To this end, we present WizardArena , a pipeline crafted to accurately predict the Elo rankings of various models using a meticulously designed offline test set. Our findings indicate that WizardArena’s predictions are closely aligned with those from the online Arena. We apply this novel framework to train our model, WizardLM- β , which demonstrates significant performance enhancements across various metrics. This fully automated training and evaluation pipeline paves the way for ongoing, incremental advancements in various LLMs via post-training. Notably, Arena Learning plays a pivotal role in the success of WizardLM-2, and this paper serves both as an exploration of its efficacy and a foundational study for upcoming discussions related to WizardLM-2 and its derivatives.

Introduction

With the rapid implementation of various large model applications and the reduction of inference costs, the interest and demand from businesses and consumers in using large language model services have increased rapidly. At the same time, with the innovation and deepening of application scenarios, this requires those models to continue to evolve to adapt to the user’s new intentions and instructions. Therefore, building an efficient data flywheel to continuously collect feedback and improve model capabilities has become a key direction for next generation AI research.

The emergence of the LMSYS Chatbot Arena has been a significant development. This is a platform that facilitates the assessment and comparison of different chatbot models by pitting them against each other in a series of conversational challenges and rank with Elo rating system. However, the human-based evaluation process poses its own challenges: Manually orchestrating and waiting the interactions between chatbots and human evaluators can be time-consuming and resource-intensive, limiting the scale and frequency of evaluation and training data opensource cycles. On the other hand, due to their priority limitations, most models are unable to participate in arena evaluations, and the community can only obtain 10% of the chat data at most, making it hard to directly and efficiently guide the development of the target model based on this Arena. Therefore, the need for a more efficient and scalable arena-based pipeline to chatbot post-training and evaluation has become increasingly pressing.

To address these challenges, this paper introduces a novel approach called  Arena Learning , which is a training and evaluation pipeline fully based on and powered by AI LLMs without human evaluators. The primary objective of  Arena Learning  is to build an efficient data flywheel and mitigate the manual and temporal costs associated with post-training LLMs while retaining the benefits of arena-based evaluation and training. As the running example shown in the Figure 1, the key is that  Arena Learning  simulates a offline chatbot arena, and can efficiently predict accurate performance rankings among different arena battle models based on a powerful “judge model”, which could automatically imitate the manner of human annotators in judging a responses pair of two models and provide rankings, scores, and explanation.

Approach Overview

In the post-training scenario,  Arena Learning  simulates battles among target model (referred to as  WizardLM-β ) and various state-of-the-art models on a large scale of instruction data. These synthetic battle results are then used to enhance  WizardLM-β  through some training strategies, including supervised fine-tuning (SFT), direct preference optimization (DPO), and proximal policy optimization (PPO), enabling it to learn from the strengths and weaknesses of other good models. Furthermore,  Arena Learning  introduces an iterative battle and training process, where the  WizardLM-β  is continuously updated and re-evaluated against SOTA models. This allows for the  WizardLM-β  to iteratively improve and adapt to the evolving landscape of the arena, ensuring that it remains competitive and up-to-date with the latest top-tier competitors in the field.

Build a Data Flywheel to Post-train LLMs

Collect large-scale instruction data.

To facilitate leveraging the simulated arena battles among models to train WizardLM-β, Arena Learning relies on a large-scale corpus of conversational data  D . The data collection process involves several stages of filtering, cleaning, and deduplication to ensure the quality and diversity of the instruction data. The simulated arena battle outcomes are then used to generate training data for the WizardLM-β, tailored to different training strategies: supervised fine-tuning (SFT), direct preference optimization (DPO), and proximal policy optimization (PPO). We split the data equally into some parts  D  = { D _0,  D _1,  D _2, …,  D _N} for following iterative training and updates respectively.

Iterative Battle and Model Evolving

Arena Learning employs an iterative process for training and improving the WizardLM-β. After each round of simulated arena battles and training data generation, the WizardLM-β is updated using the appropriate training strategies (SFT, DPO, and/or PPO). This updated model is then re-introduced into the arena, where it battles against the other SOTA models once again. This iterative process allows the WizardLM-β to continuously improve and adapt to the evolving landscape of the arena. As the model becomes stronger, the simulated battles become more challenging, forcing the WizardLM-β to push its boundaries and learn from the latest strategies and capabilities exhibited by the other models. Additionally, the iterative nature of Arena Learning enables the researchers to monitor the progress and performance of the WizardLM-β over time, providing valuable insights into the effectiveness of the different training strategies and potential areas for further improvement or refinement.

Evaluate LLMs with WizardArena

3. Experiments Overview

3.1 offline wizardarena closely align with the online lmsys chatbot arena..

Figure 6 reveals that employing the LMSYS ChatBot Arena as the reference benchmark in the real-world scenarios, WizardArena displays the good ranking consistency, however MT-Bench shows the large fluctuations. Table 3 also illustrates that the Offline WizardArena-Mix significantly outperforms MT-Bench across several consistent metrics: a 19.87% higher Spearman Correlation, a 73.07% increase in Human Agreement with 95% CI, and a 74.57% improvement in Differentiation with 95% CI. It achieves an average consistency of 98.79% with the LMSYS ChatBot Arena by human judgment, outperforming Arena-Hard-v1.0 by 8.58% and MT-Bench by 55.84%. In contrast to MT-Bench and Arena-Hard-v1.0 which use proprietary models (i.e. GPT-4) as the judge model, our approach employs current SOTA open-source model Llama-3-70B-Chat, which not only has a significantly lower cost but also achieves strong consistency. Moreover, the Offline WizardArena-Mix, which integrates both Diverse and Hard test sets, achieves 0.87% higher average consistency compared to WizardArena-Diverse and 0.82% higher than WizardArena-Hard. This indicates that balancing diversity and complexity is crucial for the effective offline evaluation of large language models. Above results also further prove the feasibility of using the “judge” model to judge the battles between LLMs and generate a large amount of post-training data in simulated arena.

3.2 Can Arena Learning build an effective data flywheel with post-training?

Following Table demonstrates the impact of using the methodname method to post-train WizardLM-beta models during three data flywheel iterations, where I_i represents the i-th iteration. In each iteration from I_1 to I_3, we always use 90k data for post-training. Starting from WizardLM-β-7B-I_0, the next 3 iterations have improved by 343 points, 32 points, and 28 points on Wizardarena-Mix Elo, respectively. At the same time, the MT-bench score of this model has also achieved significant improvement (from 6.41 to 8.16). Specifically, the WizardLM-β-7B-I_1 even surpasses WizardLM-70B-v1.0 and the WizardLM-β-7B-I_3 also shows comparable performance with Starling-LM-7B-Beta. It is worth noting that we have also observed the same trend on WizardLM-β-8x22B models, and even achieved a more significant increase in both Wizardarena-Mix Elo (+460) and MT-Bench (+2.07). This model also beats both Command R+ and Claude 3 Haiku. Following Figure presents the win rates of 32 models in WizardArena-Mix, with each model involving in 2k x 31 battles. Compared to those baselines, our model has achieved significant improvements in win rate from the I_0 to I_3. Specifically, using GPT-4o as the battle target, our WizardLM-β-8x22B’s win rate increased by 26% (8% -> 22% -> 27% ->34%), WizardLM-β-7B’s win rate also increased by 14% (6% -> 16% -> 18% ->20%).

Above results highlight that continuous battle with SOTA models with Arena Learning and updating weights with new selected data can progressively enhance model capacities compared to its rivals. Hence, Arena Learning builds an effective data flywheel and utilizing the Arena Learning can significantly improve model performance in post-training.

3.3 Scaling Iterative SFT, DPO, and PPO with Arena Learning.

As the core question of this paper asks how Arena Learning improves a model’s performance with post-training, in this section we examine how performance is affected by different post-training technology and data flywheel iterations. Following Figure explores the results of WizardLM-β-7B model. As expected, we observe that each performance across the SFT and RL models improves step by step as we add more selected data from more Arena Learning battle iterations. Specifically, from SFT-I_0 to PPO-I_3, the WizardArena-Mix ELO score improves from 871 to 1274, achieves a huge gain of 403 points, and the Arena-Hard Auto ELO score also rose by 26.3 points (from 5.2 to 31.5). Additionally, the AlpacaEval 2.0 LC win rate improved by 26%, from 8.2% to 34.2%, and the MT-Bench score increased by 1.75 points, from 6.41 to 8.16. Significant improvements across four key benchmarks highlight the effectiveness and scalability of the iterative training approach proposed by Arena Learning in enhancing post-training LLMs during the SFT, DPO, and PPO stages.

In conclusion, this paper presents Arena Learning, a simulated offline chatbot arena that utilizes AI LLMs to eliminate the manual and temporal costs associated with post-training LLMs, while preserving the benefits of arena-based evaluation and training. The effectiveness of Arena Learning is validated through the high consistency in predicting Elo rankings among different LLMs compared to the human-based LMSys Chatbot Arena. Furthermore, the model iteratively trained on synthetic data generated by Arena Learning exhibits significant performance improvements across various training strategies. This work showcases the potential of Arena Learning as a cost-effective and reliable alternative to human-based evaluation and data flywheel platform for post-training large language models.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Title: vision language models are blind.

Abstract: Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini 1.5 Pro are powering countless image-text applications and scoring high on many vision-understanding benchmarks. We propose BlindTest, a suite of 7 visual tasks absurdly easy to humans such as identifying (a) whether two circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word; and (d) counting the number of circles in a Olympic-like logo. Surprisingly, four state-of-the-art VLMs are, on average, only 56.20% accurate on our benchmark, with \newsonnet being the best (73.77% accuracy). On BlindTest, VLMs struggle with tasks that requires precise spatial information and counting (from 0 to 10), sometimes providing an impression of a person with myopia seeing fine details as blurry and making educated guesses. Code is available at: this https URL

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A new way to miniaturize cell production for cancer treatment

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Researchers from the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, have developed a novel way to produce clinical doses of viable autologous chimeric antigen receptor (CAR) T-cells in a ultra-small automated closed-system microfluidic chip, roughly the size of a pack of cards. 

This is the first time that a microbioreactor is used to produce autologous cell therapy products. Specifically, the new method was successfully used to manufacture and expand CAR-T cells that are as effective as cells produced using existing systems in a smaller footprint and less space, and using fewer seeding cell numbers and cell manufacturing reagents. This could lead to more efficient and affordable methods of scaling-out autologous cell therapy manufacturing, and could even potentially enable point-of-care manufacturing of CAR T-cells outside of a laboratory setting — such as in hospitals and wards.

CAR T-cell therapy manufacturing requires the isolation, activation, genetic modification, and expansion of a patient’s own T-cells to kill tumor cells upon reinfusion into the patient. Despite how cell therapies have revolutionized cancer immunotherapy, with some of the first patients who received autologous cell therapies in remission for more than 10 years, the manufacturing process for CAR-T cells has remained inconsistent, costly, and time-consuming. It can be prone to contamination, subject to human error, and requires seeding cell numbers that are impractical for smaller-scale CAR T-cell production. These challenges create bottlenecks that restrict both the availability and affordability of these therapies despite their effectiveness.

In a paper titled “ A high-density microbioreactor process designed for automated point-of-care manufacturing of CAR T cells ” published in the journal Nature Biomedical Engineering , SMART researchers detailed their breakthrough: Human primary T-cells can be activated, transduced, and expanded to high densities in a 2-mililiter automated closed-system microfluidic chip to produce over 60 million CAR T-cells from donors with lymphoma, and over 200 million CAR T-cells from healthy donors. The CAR T-cells produced using the microbioreactor are as effective as those produced using conventional methods, but in a smaller footprint and less space, and with fewer resources. This translates to lower cost of goods manufactured (COGM), and potentially to lower costs for patients.

The groundbreaking research was led by members of the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) interdisciplinary research group at SMART. Collaborators include researchers from the Duke-NUS Medical School; the Institute of Molecular and Cell Biology at the Agency for Science, Technology and Research; KK Women’s and Children’s Hospital; and Singapore General Hospital.

“This advancement in cell therapy manufacturing could ultimately offer a point-of-care platform that could substantially increase the number of CAR T-cell production slots, reducing the wait times and cost of goods of these living medicines — making cell therapy more accessible to the masses. The use of scaled-down bioreactors could also aid process optimization studies, including for different cell therapy products,” says Michael Birnbaum, co-lead principal investigator at SMART CAMP, associate professor of biological engineering at MIT, and a co-senior author of the paper.

With high T-cell expansion rates, similar total T-cell numbers could be attained with a shorter culture period in the microbioreactor (seven to eight days) compared to gas-permeable culture plates (12 days), potentially shortening production times by 30-40 percent. The CAR T-cells from both the microfluidic bioreactor and gas-permeable culture plates only showed subtle differences in cell quality. The cells were equally functional in killing leukemia cells when tested in mice.

“This new method suggests that a dramatic miniaturization of current-generation autologous cell therapy production is feasible, with the potential of significantly alleviating manufacturing limitations of CAR T-cell therapy. Such a miniaturization would lay the foundation for point-of-care manufacturing of CAR T-cells and decrease the “good manufacturing practice” (GMP) footprint required for producing cell therapies — which is one of the primary drivers of COGM,” says Wei-Xiang Sin, research scientist at SMART CAMP and first author of the paper.

Notably, the microbioreactor used in the research is a perfusion-based, automated, closed system with the smallest footprint per dose, smallest culture volume and seeding cell number, as well as the highest cell density and level of process control attainable. These microbioreactors — previously only used for microbial and mammalian cell cultures — were originally developed at MIT and have been advanced to commercial production by Millipore Sigma.

The small starting cell numbers required, compared to existing larger automated manufacturing platforms, means that smaller amounts of isolation beads, activation reagents, and lentiviral vectors are required per production run. In addition, smaller volumes of medium are required (at least tenfold lower than larger automated culture systems) owing to the extremely small culture volume (2 milliliters; approximately 100-fold lower than larger automated culture systems) — which contributes to significant reductions in reagent cost. This could benefit patients, especially pediatric patients who have low or insufficient T-cell numbers to produce therapeutic doses of CAR T-cells.

Moving forward, SMART CAMP is working on further engineering sampling and/or analytical systems around the microbioreactor so that CAR-T production can be performed with reduced labor and out of a laboratory setting, potentially facilitating the decentralized bedside manufacturing of CAR T-cells. SMART CAMP is also looking to further optimize the process parameters and culture conditions to improve cell yield and quality for future clinical use.

The research was conducted by SMART and supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

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3.3 Scaling Iterative SFT, DPO, and PPO with Arena Learning. As the core question of this paper asks how Arena Learning improves a model's performance with post-training, in this section we examine how performance is affected by different post-training technology and data flywheel iterations.

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Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini 1.5 Pro are powering countless image-text applications and scoring high on many vision-understanding benchmarks. We propose BlindTest, a suite of 7 visual tasks absurdly easy to humans such as identifying (a) whether two circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word ...

Researchers devised a way to miniaturize cell production for cancer treatment. The method for producing CAR-T cells, developed at the Singapore-MIT Alliance for Research and Technology, uses fewer resources and a smaller footprint than existing automated manufacturing platforms and could lead to more affordable methods of cell therapy manufacturing.

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However, these scores are often most accurate for people of European ancestry, because past research left out many other groups. Researchers adjusted polygenic risk scores for 10 common conditions using the diverse genetic data in All of Us so that the scores are accurate for people from a variety of backgrounds.

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Crafting Good Prompts

The clear framework, writing prompts for images.

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Other Prompt Engineering Frameworks

The Prompt Engineering Guide by DAIR.AI has many more techniques and guidelines for prompt engineering:

• General Tips for Prompts
• Chain-of-Thought Prompting
• Generate Knowledge Prompting

A  prompt  is an input or query to an AI tool. For generative AI (GAI) tools, the prompt is almost always a question, request, or topic posed by you, the human researcher. The rise of GAI has led to  prompt engineering —or the process of constructing queries or inputs for AI language models so as to elicit the most precise, coherent, and pertinent responses, (Lo, 2023). Why should you try to write good prompts?

• Good prompts help the AI help you.  A well-crafted prompt enables the AI to give you meaningful and useful results. A bad prompt may result in irrelevant data or lead you away from the best research.
• Good prompts are cheap(er).  Many AI research tools charge per task - each search costs you “credits” and (ultimately) money. A good prompt can help you get the desired output without a lot of repeated searches, easier on the pocketbook and a less waste of time for you.
• Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship , 49(4), 102720–. https://doi.org/10.1016/j.acalib.2023.102720

Good prompts are CLEAR – a framework developed by Leo Lo, a librarian and professor at the University of New Mexico.

Even the best prompts may need improvement! The last two components address what to do after you’ve examined the AI’s answer to your initial prompt.

• Strunk, W. & White, E.B. (2005).  The Elements of Style.  Penguin. (Original work published 1979) 
• " Image Generation: Usage ." (n.d.)  OpenAI Documentation . Retrieved October 26, 2023, from  https://platform.openai.com/docs/introduction Techniques on working with DALL-E (OpenAI's image tool)
• Rebelo, M. (2023, May 25).  How to write effective AI art prompts . Zapier.com.  https://zapier.com/blog/ai-art-prompts/ A quick but very useful read on crafting prompts for AI image tools.
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Seriously questioning whether prompt generators for generative ai that produce ai-written prompts are better than human-devised prompts.

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Assessing whether prompt generators in generative AI are worth their hype.

In today’s column, I am continuing my ongoing coverage of prompt engineering strategies and tactics that aid in getting the most out of using generative AI apps such as ChatGPT, GPT-4, Bard, Gemini, Claude, etc. The focus this time will be on assessing whether a prompt generator can do as good a job, worse of a job, or produce better prompts than a human can when composing prompts for generative AI.

Readers might recall that I previously provided a handy overview of the rising trend toward making use of prompt generators, see the link here .

A prompt generator is essentially the use of generative AI to generate prompts for you. The straightforward idea is that you tell AI what aspect you want to ask about or indicate a problem that you want to have solved, and voila, a knock-your-socks-off prompt will be generated accordingly.

You can then feed that prompt into the same generative AI or use it in a different generative AI app.

All in all, the declared purpose is to reduce your efforts in composing prompts and alleviate any frustrations or exasperations while doing so. Just lackadaisically have AI do the heavy lifting that will presumably incorporate the best of prompt engineering precepts when devising a desired prompt since that’s something generative AI should presumably be data trained on. In a sense, using a prompt generator is like having an automated “prompt engineer” looking over your shoulder and doing the hard work for you.

Does the real world achieve this promise?

Let’s put this to the test and see if the promise lives up to the hype.

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Before we leap into the matter at hand, and for those of you interested in prompt engineering overall, you might find especially helpful my comprehensive guide covering over fifty keystone prompting strategies, see the discussion at the link here .

The Nature Of Prompt Generators

I trust that my above brief description gave you a general sense of what prompt generation is all about.

Here’s the full deal.

You describe to a prompt generator the overall semblance of what you want to ask generative AI to do, and the prompt generator will generate a suitable prompt for you. Easy-peasy. This is as easy as falling off a log.

If you’ve never tried using a prompt generator the concept of it might seem at initial glance a bit askew. Why go to the trouble of describing what you want to ask? By gosh, just go ahead and ask what you want to ask, doing so by writing your own prompts directly. The prompt generator seems like a totally unneeded middleman.

Well, some people aren’t versed in prompt engineering and they are unfamiliar with the best ways to compose prompts. They could spend the time to learn how and become proficient at doing so. On the other hand, they could allow a prompt generator to shoulder the burden for them.

Another reason that prompt generators are handy is that even if you know how to suitably write prompts, the prompt generator potentially provides an added edge. Maybe you’ve forgotten some of those trickery phrases that you can use to sweeten a prompt and make it more powerful. Perhaps you are tired of writing prompts and want to take a rest by having AI do the effort on your behalf.

One issue that confronts the use of prompt generators is that you might not relish the prompt that a prompt generator produces. It could be that you examine the produced prompt and realize it is far afield from what you intended.

I think you can detect a vicious cycle in this.

You start spending more time trying to get the prompt generator to refine and generate a suitable prompt than if you had merely written the darned prompt yourself. At the get-go, you would have likely had a less cantankerous time by simply doing the work yourself. How far down the rabbit hole should you go when trying to push the prompt generator in the direction of what you already had firmly devised in your mind?

The gist is that prompt generators are not for everyone.

When I say that, you can expect some pundits to gasp in despair. A commonly touted prediction is that people will soon no longer need to write prompts. Prompt generators will be the norm. You tell the prompt generator what you want, and it communicates with generative AI. You no longer directly enter prompts. That will be considered old hat and out of favor.

I do not subscribe to that so-called replacement theory, and certainly not in the near term. I assert that we will still be writing prompts for quite a while. I then also assert that knowing something about prompt engineering is quite useful and worthy of your energy and time. My analogy is that we have said for the last fifty years or more that programming was going to be washed up and nobody would ever write a line of code again. Pundits have repeatedly exhorted that it is a dead-end to learn how to program.

Ahem, allow me to clear my throat. Estimates reported online suggest that there are at least 20 to 30 million software developers in the world today and that the number continues to grow (Source: “How Many Developers In The World: Latest And Unbiased Data” by Phu Nguyen, Tech News , January 3, 2024). Prospects remain bright. Salaries remain high.

Prompt engineering is likewise here to stay for a while.

Prompt generators will definitely have their place. I have predicted a huge growth in the number of prompt generators and their prowess. This though doesn’t equate to declaring handwritten prompts as dead or buried. The two approaches will co-exist.

On Using Prompt Generators And How To Do So

Your interest in prompt generators is now hopefully piqued.

Time to further unpack the weighty matter.

There are two major types of prompt generators:

• (1) External prompt generator . A specialized tool that sits outside of a generative AI app and acts as a front-end to generative AI that will compose your prompt and feed it into the generative AI app of your choosing.
• (2) Internal prompt generator . Make use of your generative AI app as a prompt generator by instructing the generative AI to act in that fashion.

My preference is the Internal Prompt Generator. That’s what I will be delving into here. You are welcome though to look online at the various External Prompt Generator tools. I also discuss those further in my examination of the trend toward trust layers being built to surround generative AI as a wraparound, see my coverage at the link here .

In the case of Internal Prompt Generators, you simply instruct generative AI to take your entered descriptions and turn those into prompts for you. I will be showing you examples of this later. I used ChatGPT as my generative AI app and told it to generate prompts for me. Hang in there, you’ll likely find those examples valuable.

If you are going to take this route, I’ve also covered the variety of ways that you can use generative AI to improve your prompting skills too, see my discussion at the link here . You see, I have formulated a set of rules of thumb about how to best use prompt generation to generate personalized prompts for you.

Here are my handy tips that I cover during my classes that I teach on prompt engineering:

• (1) Find out about good prompting . Use generative AI to explain to you how to write good prompts.
• (2) Get feedback on a hand-crafted draft prompt . Show generative AI a prompt you’ve crafted and get feedback about how it can be improved.
• (3) Generate a draft prompt . Tell generative AI to go ahead and create a draft prompt for you.
• (4) Obtain feedback about a performed prompt . After a prompt has been performed and a result obtained, ask generative AI to evaluate the prompt and ascertain if a better version might produce better results.
• (5) Dovetail a prompt draft request into prompt performance . Once you’ve gotten a generative AI drafted prompt, you can go ahead and have generative AI perform the prompt.
• (6) Analyze your series of prompts and your prompting strategies. You can ask generative AI to assess a series of prompts that you’ve crafted and used, aiming to see what prompting patterns of improvement might be discovered by the AI in your prompting style and strategy.

The takeaway is that besides using generative AI to be your prompt generator, you can think further outside the box and use generative AI to guide, review, and improve your prompting expertise.

An Example Of A Prompt Generator

There’s an example of a prompt generator that I’d like to share with you. One of the major generative AI apps is made by a company called Anthropic. Their generative AI mainstay app is named Claude.

I bring this up because they have posted online the source code for a prompt generator that you can play with in Claude. I will briefly go over their approach here. If you have a Claude account, you might consider playing with the prompt generator to gauge what it does and whether you want to regularly use this one or indeed any prompt generator.

For those that don’t use Claude, no worries as I will only lightly touch on the nature of this particular prompt generator for purposes of illustration. Once we get into the ChatGPT examples, you’ll see more details associated with doing similar prompt generator functionality while in ChatGPT or just about any generative AI app.

On the Anthropic website, there is a subsection entitled “Prompt Generator” and it says this (excerpts):

• “Sometimes, the hardest part of using an AI model is figuring out how to prompt it effectively.”
• “To help with this, we’ve created a prompt generation tool that can guide Claude to generate a high-quality prompt tailored to your specific tasks. The prompt generator is particularly useful as a tool for solving the “blank page problem” and giving you a jumping-off point for testing and iteration that follows some of our best practices like chain of thought separating data from instructions.”
• “Try the prompt generator now directly on the Console.”
• “If you’re interested in analyzing the underlying prompt and architecture, check out our prompt generator Google Colab notebook. There, you can easily run the code to have Claude construct prompts on your behalf.”
• “Note that to run the Colab notebook, you will need an API key.”

When you run the prompt generator, this is essentially what you see:

• “Welcome to the Metaprompt! This is a prompt engineering tool designed to solve the "blank page problem" and give you a starting point for iteration. All you need to do is enter your task, and optionally the names of the variables you'd like Claude to use in the template. Then you'll be able to run the prompt that comes out on any examples you like.”
• “This is designed for single-turn question/response prompts, not multiturn.”
• “The Metaprompt is designed for use with Claude 3 Opus. Generating prompts with other models may lead to worse results.”
• “The prompt you'll get at the end is not guaranteed to be optimal by any means, so don't be afraid to change it!”

I wanted you to see those bulleted points so that you can be aware of potential limitations associated with prompt generators. Various prompt generators will have differing capabilities and limitations.

In this case, they are telling you that the prompt generator is aimed at a single turn. This means that the prompt will be used just for one step. One and done. So what? Well, if the generative AI responds to the prompt, and you are supposed to enter another prompt, you will need to use the prompt generator a second time. And so on.

Some prompt generators will aid you instead on a multi-turn basis. Here’s how that works. You enter a description into the prompt generator about whatever topic, question, or matter that you want to discuss with generative AI. The prompt generator will generate an initiating prompt. This will be fed into the generative AI. In addition, the prompt generator stays in the loop and will generate additional prompts, dependent upon whatever the generative AI is responding with.

Another important point made in the above limitations is that you ought to not assume that the prompt generator is going to generate the best-ever prompt for you. Likely not. It will potentially be a good prompt, but it won’t achieve some worldwide award or be crowned as top of the world.

You might be curious about how the prompt generator works.

I’m glad you asked.

On the Anthropic website where they have the source code, they say this:

• “The Metaprompt is a long multi-shot prompt filled with half a dozen examples of good prompts for solving various tasks. These examples help Claude to write a good prompt for your task. The full text is below (warning: it's long!).”

Allow me to explain.

The usual method of getting generative AI to be a prompt generator is to give a prompt to the generative AI that tells the AI to henceforth be a prompt generator. You simply write a prompt that says in the simplest of terms that the AI is to start generating prompts for you. This can be thought of as a seed prompt. Plant a seed, and watch it grow.

You could also be more extensive in giving detailed guidance to generative AI about what you want the prompt generator to be able to do. That’s what Anthropic decided to do. They first came up with examples of what they considered useful prompts. These serve as examples that generative AI can pattern onto. Next, they provided an instruction that tells generative AI to be a prompt generator.

This is a two-step process of providing examples and followed with a specific set of instructions.

They make the source publicly available.

Let’s take a quick look at it.

At that same Anthropic website mentioned above, they provide various examples of what they consider to be useful prompts (these are excerpts, please take a look online to see the full text):

• “metaprompt = '''Today you will be writing instructions to an eager, helpful, but inexperienced and unworldly AI assistant who needs careful instruction and examples to understand how best to behave. I will explain the task to you. You will write instructions that will direct the assistant on how best to accomplish the task consistently, accurately, and correctly. Here are some examples of tasks and instructions.”
• “<Task Instruction Example>: Act as a polite customer success agent for Acme Dynamics. Use FAQ to answer questions. You will be acting as an AI customer success agent for a company called Acme Dynamics…”
• “</Task Instruction Example>: Check whether two sentences say the same thing. You are going to be checking whether two sentences are roughly saying the same thing…”
• “<Task Instruction Example>: Answer questions about a document and provide references I'm going to give you a document. Then I'm going to ask you a question about it…”
• “<Task Instruction Example>: Act as a math tutor. A student is working on a math problem. Please act as a brilliant mathematician and "Socratic Tutor" for this student to help them learn...”
• “</Task Instruction Example>: Answer questions using functions that you're provided with. You are a research assistant AI that has been equipped with the following function(s) to help you answer a <question>…”

Here is a snippet of the set of instructions (again, excerpted, thus please take a look at their website to see the full text):

• “To write your instructions, follow THESE instructions.”
• “1. In <Inputs> tags, write down the barebones, minimal, nonoverlapping set of text input variable(s) the instructions will make reference to…”
• “2. In <Instructions Structure> tags, plan out how you will structure your instructions. In particular, plan where you will include each variable…”
• “3. Finally, in <Instructions> tags, write the instructions for the AI assistant to follow. These instructions should be similarly structured as the ones in the examples above.”
• “Note: This is probably obvious to you already, but you are not *completing* the task here. You are writing instructions for an AI to complete the task.”
• “Note: Another name for what you are writing is a "prompt template". When you put a variable name in brackets + dollar sign into this template, it will later have the full value (which will be provided by a user) substituted into it…”
• “Note: When instructing the AI to provide an output (e.g., a score) and a justification or reasoning for it, always ask for the justification before the score.”
• “Note: If the task is particularly complicated, you may wish to instruct the AI to think things out beforehand in scratchpad or inner monologue XML tags before it gives its final answer. For simple tasks, omit this.”
• “Note: If you want the AI to output its entire response or parts of its response inside certain tags, specify the name of these tags (e.g. "write your answer inside <answer> tags") but do not include closing tags or unnecessary open-and-close tag sections.'''

I won’t get into the details of those examples and instructions. I wanted you to generally see what kinds of instructions were used. This gives you insight into how to instruct generative AI to be a prompt generator, doing so in a more elaborate manner.

I will be showing you next the use of ChatGPT with a much simpler approach.

Using ChatGPT To Explore Prompt Generators

I will next proceed to examine further the nature of prompt generators. This will consist of a series of dialogues with ChatGPT. ChatGPT is a logical choice in this case due to its immense popularity as a generative AI app. An estimated one hundred million weekly active users are said to be utilizing ChatGPT. That’s a lot of people and a lot of generative AI usage underway.

A few quick comments before we launch into using ChatGPT.

If you are going to try to do the same prompts that I show here, realize that the probabilistic and statistical properties will likely produce slightly different results than what I show here. That’s the nature of generative AI and how it is devised.

You also should expect that different generative AI apps will respond in different ways. Not all generative AI apps are the same. The key is that sometimes a particular prompt will work in one generative AI app and not another. Nonetheless, the odds are relatively high that you will get roughly similar responses from all the major generative AI apps such as GPT-4, Gemini, Bard, Claude, etc.

There is also a timing factor to be considered. A prompt that worked today might not work the same way tomorrow. Most of the generative AI apps are continually being updated. The updates might alter internal mechanisms that could change how the generative AI reacts to your prompting.

We are ready to get underway with ChatGPT.

I opted to start by asking ChatGPT about the overall nature of prompt generators. We will likely find out which way ChatGPT goes on this matter.

As an aside, whenever you are starting a conversation with generative AI, I recommend as a prompt engineering technique to begin by asking a question that will establish if the AI has been data-trained on the topic at hand. A generated response that is vacuous will give you a heads-up that you might be barking up the wrong tree with that generative AI app.

Here we go.

• My entered prompt : “What are the advantages of using generative AI to generate my prompts for me?”
• ChatGPT generated response:

“Using generative AI to create prompts can offer several advantages.”

“ Variety : Generative AI can generate a wide range of prompts across different topics and styles, providing you with diverse ideas to explore.”

“ Inspiration : AI-generated prompts can spark creativity by presenting new perspectives and ideas that you might not have thought of on your own.”

“ Efficiency : It saves time and effort by quickly generating multiple prompts, allowing you to focus on the creative process rather than brainstorming initial ideas.”

“ Customization : You can tailor the prompts to suit your specific needs, such as focusing on a particular theme, tone, or complexity level.”

“ Consistency : AI-generated prompts can maintain a consistent quality and style, which can be helpful for maintaining a cohesive narrative or theme in your work.”

“ Feedback Loop : By using AI-generated prompts, you can receive feedback on your responses, helping you improve your writing skills over time.”

“ Exploration : AI-generated prompts can encourage you to explore new genres, writing styles, or concepts that you might not have considered otherwise.”

End of ChatGPT response

Those are sensible reasons to use a prompt generator.

I think it is important to also be aware of the downsides.

Let’s ask about that.

• My entered prompt : “What are the downsides of using generative AI to generate my prompts for me?”

“While generative AI can offer many benefits, there are also some potential downsides to using it to generate prompts.”

“ Lack of Human Touch : AI-generated prompts may lack the human touch and emotional depth that can come from prompts created by humans. They might feel mechanical or formulaic at times.”

“ Quality Control : AI-generated prompts can vary widely in quality, and there's a risk of receiving prompts that are nonsensical, irrelevant, or of low quality, which can be frustrating and time-consuming to filter through.”

“ Originality Concerns : There's a possibility that AI-generated prompts may unintentionally resemble existing prompts or ideas, leading to concerns about originality and plagiarism.”

“ Limited Context Understanding : Generative AI may not fully understand the context or nuances of your writing project or creative goals, resulting in prompts that are not aligned with your needs or vision.”

“ Dependency : Relying too heavily on AI-generated prompts can lead to a dependency on the AI system, potentially hindering your own creative thinking and idea generation skills over time.”

“ Ethical Considerations : Using AI for prompts raises ethical questions about the role of technology in creative processes, as well as concerns about data privacy and AI biases.”

“ Technical Issues : AI systems can sometimes encounter technical issues or errors, leading to disruptions in prompt generation or inaccurate outputs.”

For my money, the biggest of the downsides is that the generated prompt might not adequately represent what you had in mind. This can lead to an inadvertent and wasteful vicious cycle per my earlier comments on the matter.

Some of the listed downsides are worthy of deep contemplative thought.

For example, one postulated concern is that people might become overly reliant on prompt generators. Perhaps humanity will lose the ability to compose our own prompts. Seems a bit farfetched, but you are welcome to grab a cup of coffee, relax in a comfy chair, and mull over the possibilities.

We are now ready to shift into gear and aim at using ChatGPT as a prompt generator.

This can be easily undertaken by giving instructions to ChatGPT. You can be brief and simply say “Start being a prompt generator” or something to that effect. I usually give a bit more detail. Some people go whole-hog and provide extensive instructions.

After I demonstrate my set of examples to you, you will be ready to decide for yourself what kind of instructions you might want to use to initiate having generative AI such as ChatGPT act as a prompt generator. My version here is readily used on just about any major generative AI app. If there is some specific aspect of your preferred generative AI app that you believe should be invoked while it is doing prompt generation, make sure to tailor a generic set of instructions accordingly.

Here's my version of a seed prompt to get generative AI to be a prompt generator:

• “You are to be a prompt generator. You will take as input an initial prompt from the user and then produce a better prompt that could be used by the user instead of using their initial input prompt. To improve the initial prompt, use everything you know about how to best compose prompts. For example, if the prompt is too short you are to lengthen the prompt accordingly. If the prompt is overly long and convoluted, you are to shorten the prompt as best as possible. If the prompt is confusing, then straighten out the confusion and make the prompt as clearly stated as possible. If the prompt doesn’t use suitable prompt engineering principles or phrases, change the prompt to include such principles or phrases. All in all, your goal is to take the initial prompt and improve it to be a much better prompt. After displaying the improved prompt that you came up with, provide an explanation of what you did to make the initial prompt into a better prompt.”

Let’s give this a whirl.

I will start by telling ChatGPT the above set of instructions. After doing so, I will then indicate that I want a prompt generated that will aid in telling me about Abraham Lincoln.

• My entered prompt: “ I have provided you with my instructions on being a prompt generator. Let’s get underway.”
• ChatGPT generated response: “Sure, please provide the initial prompt you'd like to improve, and I'll generate a better version for you along with an explanation of the improvements made.”
• My entered prompt: “Tell me why Abraham Lincoln was famous.”

“Improved Prompt: "Provide a detailed explanation of why Abraham Lincoln is considered a significant figure in American history, highlighting his key achievements, leadership during the Civil War, and contributions to the abolition of slavery."

“Explanation.”

“Clarity: The improved prompt specifies exactly what information is being sought – an explanation of Abraham Lincoln's significance.”

“Detail: It asks for a "detailed explanation," encouraging a comprehensive answer rather than a brief one.”

“Focus Areas: By highlighting specific areas (key achievements, leadership during the Civil War, contributions to the abolition of slavery), the prompt directs the response to cover crucial aspects of Lincoln's fame.”

“Historical Context: Including the term "American history" situates Lincoln within the broader context, ensuring the explanation relates his significance to the overall historical narrative.”

I hope you were quick to compare the description that I provided versus the prompt that ChatGPT generated for me.

As a reminder, they were:

• My description: “Tell me why Abraham Lincoln was famous.”
• ChatGPT generated this prompt : "Provide a detailed explanation of why Abraham Lincoln is considered a significant figure in American history, highlighting his key achievements, leadership during the Civil War, and contributions to the abolition of slavery."

What do you think of the prompt that ChatGPT generated?

It certainly is much more detailed than the description I provided. The good news is that if I fed the generated version into generative AI, the odds are that I would get a response that would be much more on target and embellished. The bad news is that maybe I didn’t intend to have the prompt get into aspects of the Civil War, abolition, etc. I might have had something else in mind.

Shifting gears, here’s a bit of a trick that few people consider using.

You can take a prompt that was generated by a prompt generator and feed the prompt into the prompt generator again.

Mind-bending! The reason you might do so is that the prompt might be further improved. Smiley face. Of course, the prompt might get wrecked by having a second shot at it. Sad face.

• My entered prompt: “Provide a detailed explanation of why Abraham Lincoln is considered a significant figure in American history, highlighting his key achievements, leadership during the Civil War, and contributions to the abolition of slavery.”

Improved Prompt: "Explain why Abraham Lincoln is a pivotal figure in American history. Discuss his key achievements, his leadership during the Civil War, and his contributions to the abolition of slavery. Provide specific examples and historical context to support your explanation."

“Clarity and Conciseness: The improved prompt is more concise, making it easier to understand without losing the essence of the original request.”

“Specificity: It explicitly asks for specific examples and historical context, guiding the respondent to provide detailed and well-supported information.”

“Structured Request: By breaking down the request into key achievements, leadership during the Civil War, and contributions to the abolition of slavery, it provides a clear structure for the response.”

“Action-Oriented Language: Using "Explain" and "Discuss" clearly indicates the action required, making it more directive and easier to follow.”

I’m not convinced that the newly proffered prompt is much better than the prompt that I fed in.

What do you think?

Generally, there is usually a fast-diminishing point of return involved. The initial suggested prompt by a prompt generator is usually pretty good. Feeding this into the prompt generator is only going to likely achieve minor improvements, if any. The danger too is that in an effort to make the prompt better, the AI accidentally makes it worse.

My rule of thumb is that rarely would you need to feed a generated prompt into a prompt generator. That being said, it is a clever technique and one that you should keep in the back of your mind, available when needed.

I’d like to next show you what can happen when a prompt generator does too much work for you.

I am going to propose something as common as apple pie.

Let’s see what the prompt generator ends up giving us.

• My entered prompt : “Why is the sky blue?”

“Improved Prompt: "Explore the scientific explanation behind the blue color of the sky, delving into concepts such as Rayleigh scattering, wavelengths of light, and atmospheric composition."

Explanation.

“Specificity: The improved prompt specifies the topic to be explored – the scientific explanation for the blue color of the sky.”

“Scientific Focus: By mentioning "Rayleigh scattering, wavelengths of light, and atmospheric composition," the prompt directs the response towards a detailed scientific explanation, encouraging an in-depth discussion rather than a simple answer.”

“Clarity: The prompt is clear and concise, avoiding ambiguity and ensuring that the focus remains on the scientific aspects of the topic.”

Notice that my snappy input was that I wanted to know why the sky is blue.

The prompt generator turned this into a lengthy scientific rendering: “Explore the scientific explanation behind the blue color of the sky, delving into concepts such as Rayleigh scattering, wavelengths of light, and atmospheric composition.” Wow, that’s a mouthful.

Part of the problem with this generated prompt is that such a prompt will almost certainly prod generative AI into responding with a detailed scientific response. If I instead merely asked why the sky is blue, the chances are that the answer would be provided as though appealing to a fifth grader. Did I want a simple answer or a complex answer? Based on the generated prompt, it’s surely going to be a complex answer.

My point with this example was to demonstrate that sometimes a prompt generator might turn a molehill into a mountain. Stay on your toes accordingly.

For my last example, I want to show you that prompt generation can be used in specific domains. I have extensively been analyzing and covering the latest trends in generative AI for mental health, see the link here . Suppose I wanted to use the prompt generator for that specific domain.

Let’s see what happens.

Remember that my input is going to be used for ChatGPT to generate a prompt for me. I mention this because it is sometimes jarring to see a question entered into generative AI and not see a response that answers that question. We have told ChatGPT to be in a mode of working as a prompt generator. I would need to tell it to stop doing so and answer my prompts if that’s what I wanted to do.

• My entered prompt: “My friend says they have a mental health issue. What should I do?”

“Improved Prompt: "Provide guidance on how to support a friend who has disclosed a mental health issue. Discuss strategies for offering empathy, encouraging professional help, and maintaining open communication."

“Clarity: The improved prompt clearly outlines the situation – a friend disclosing a mental health issue – and specifies the desired guidance.”

“Comprehensive Support: It asks for strategies beyond just acknowledgment, such as offering empathy, encouraging professional help, and maintaining open communication, which are crucial aspects of supporting someone with a mental health issue.”

“Action-Oriented: The prompt prompts actionable advice rather than just general information, ensuring a practical and helpful response.”

The generated prompt was much more involved than my input description of the situation.

We went from my asking what I should do about a friend who says they have a mental health issue to this generated prompt: “Provide guidance on how to support a friend who has disclosed a mental health issue. Discuss strategies for offering empathy, encouraging professional help, and maintaining open communication."

Admittedly, I do like this generated prompt because it specifies that the AI is supposed to explain how to offer empathy and encourage professional help, along with ensuring open communication. Those are facets that might not have occurred to me. I was just reflexively asking what I could do about my friend. The generated prompt added value to what I was otherwise quite vague about.

There you go, score a point for the prompt generator.

You’ve now gotten a solid overview of what prompt generation consists of. In addition, I provided examples in ChatGPT. All in all, I would say you are ready to start experimenting on your own.

My big question for you is whether you see any value in using a prompt generator. Perhaps you think this is the best thing since sliced bread. Contrarily, maybe you don’t see the hubbub about it and aren’t going to rush to use a prompt generator.

Is this your cup of tea?

I can tell you something that either way will be important to know. Prompt generators are going to be flooding our use of generative AI. You will likely have a hard time avoiding them. They will be available, and many will choose to use them. I dare say that some generative AI apps might require that you use their prompt generator, whether you want to or not.

One final thought for now.

During the classes that I teach on prompt engineering, I’ve had some attendees liken the prompt generators to the use of a car. They say that the act of not using prompt generators is like driving a stick shift or manual transmission. Using a prompt generator is like driving a car with an automatic transmission. This analogy suggests that if you do use a prompt generator you are losing a sense of the grip of the roadway and not able to finely drive the car. The retort is that with an automatic transmission, you don’t have to consume excess effort on shifting the car and will instead let the automation do so for you.

Today, most cars come with an automatic transmission. That’s probably an apt indication of where things will go with generative AI. Prompt generators will be a dime a dozen.

You have to say to yourself currently, which do you prefer? Are you a stick person or an automatic transmission person? Make sure to take both for a drive so that you can make a mindful choice.

Please drive safely and mindfully in whichever mode you prefer.

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