Efficient multi-prompt evaluation of LLMs

Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin·May 27, 2024

Summary

PromptEval is a method introduced to efficiently evaluate large language models across diverse prompt templates, addressing the issue of limited benchmark prompts. It uses item response theory to estimate performance distributions and quantiles with practical budgets, providing a more robust measure of LLM capabilities. The technique is consistent and demonstrated on MMLU, BIG-bench Hard, and LMentry datasets, showing its effectiveness in estimating performance with limited evaluations. PromptEval estimates performance by leveraging information across prompts and examples, and its variants, such as PE-Rasch and PE-EmbFT, outperform baselines in estimating distribution and quantile errors. The study also highlights the importance of prompt sensitivity and the need for consistent templates in LLM evaluations.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of robustly evaluating Language Model Models (LLMs) by proposing a method that minimizes dependence on a single prompt template and provides a holistic summary of performance across a broad set of templates . This problem is not entirely new, as previous benchmarks faced challenges in determining which single prompt to use, while the current challenge lies in selecting a set of prompt templates for evaluation . The paper emphasizes the importance of evolving prompt candidates dynamically to enhance evaluation methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that common evaluation methods for large language models (LLMs), which often rely on a single or limited number of prompt templates, may not adequately reflect the typical model's capabilities. This limitation can lead to unreliable and inconsistent rankings on LLM leaderboards, as different models may perform differently based on the specific prompt template used. The paper suggests the need for an evaluation framework that minimizes dependence on any single prompt template and provides a holistic summary of performance across a broad set of templates .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models for efficient multi-prompt evaluation of LLMs:

  • Dynamic Prompt Generation: The paper suggests dynamically generating new prompt candidates to enhance evaluation. Prasad et al. introduce an evolutionary algorithm that creates new prompts based on previously successful ones, allowing for an evolving set of prompt candidates.
  • Holistic Evaluation Framework: It emphasizes the need for a holistic evaluation framework that minimizes reliance on a single prompt template. Weber et al. argue that traditional evaluation methods using limited prompt templates may not fully capture the capabilities of language models, leading to inconsistent rankings on leaderboards.
  • Multiple Prompt Templates: The study advocates for the use of multiple prompt templates to provide a comprehensive summary of model performance across various prompts. Mizrahi et al. highlight the importance of considering a diverse set of prompt variations to assess model performance accurately.
  • Adapting Correctness Models: The paper addresses situations where the response variable is bounded or continuous, suggesting adjustments to the correctness model for evaluation. It discusses potential fixes such as using the Beta model or binarizing the response variable .
  • Resource Utilization: The experiments conducted in the study were carried out using a virtual machine with 32 cores, and the results for each benchmark could be obtained within 3-6 hours .

These proposed ideas and methods aim to enhance the evaluation of LLMs by introducing dynamic prompt generation, advocating for a holistic evaluation approach, utilizing multiple prompt templates, adapting correctness models, and detailing the computational resources required for experimentation. The paper on efficient multi-prompt evaluation of LLMs introduces several novel characteristics and advantages compared to previous methods:

  • Dynamic Prompt Generation: The paper proposes dynamically generating new prompt candidates based on successful ones from earlier iterations, enhancing the adaptability of the evaluation process .
  • Holistic Evaluation Framework: It emphasizes the importance of a holistic evaluation approach that considers a diverse set of prompt templates to provide a comprehensive assessment of language models' capabilities .
  • Efficient Performance Distribution Computation: The study addresses the challenge of efficiently computing the performance distribution of LLMs over multiple prompt templates, aiming to streamline evaluations and reduce costs .
  • Adapting Correctness Models: The paper discusses adjustments to correctness models for bounded or continuous response variables, ensuring accurate evaluation under different scenarios .
  • Resource Utilization: The experiments conducted in the study were carried out using a virtual machine with 32 cores, providing insights into the computational resources required for efficient evaluation .

These characteristics highlight the paper's contributions in enhancing evaluation processes through dynamic prompt generation, holistic evaluation frameworks, efficient performance distribution computation, adapting correctness models, and considerations for resource utilization in LLM evaluations.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field, and there are noteworthy researchers who have contributed significantly to this topic. Some of the notable researchers mentioned in the context include Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal, Nils Reimers, Iryna Gurevych, Sidney Resnick, Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, and many others . These researchers have made valuable contributions to areas such as prompt-based learning, language model evaluation, and item response theory.

The key to the solution mentioned in the paper involves dynamically generating new prompt candidates. Researchers like Prasad et al. proposed an evolutionary algorithm that creates new prompts based on those that performed well in earlier iterations . This approach demonstrates the benefits of evolving prompt candidates to enhance the performance of large language models in various tasks. By adapting and generating new prompts dynamically, researchers aim to improve the efficiency and effectiveness of prompt-based learning and evaluation processes in the field of natural language processing.


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting experiments using a virtual machine with 32 cores for each benchmark separately, with results obtainable within 3-6 hours . The training data consisted of concatenated prompting templates with all Example ID tokens, resulting in different dataset sizes for each benchmark . The experiments involved training on an iid split of half of the LLMs at a time and testing on the other half, with the training data split into an 80% training and 20% validation set . Additionally, a small grid search was performed over different hyperparameter settings, settling on specific setups such as using the Adam optimizer with an initial learning rate of 2e-5 and a weight decay of 1e-5, along with other parameters like batch size and learning rate adjustments .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is derived from three popular benchmarks: MMLU, BIG-bench Hard (BBH), and LMentry . The code used in the study is open source, as it is mentioned that the evaluation data will be released .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The paper includes detailed proofs and lemmas to support the theoretical framework of the research . Additionally, the references to prior works and related studies demonstrate a comprehensive understanding of the existing literature and build upon previous research findings.

The experiments conducted in the paper involve complex mathematical analyses and statistical evaluations, such as the use of the Dominated Convergence Theorem, Fubini’s Theorem, and the Lebesgue Dominated Convergence Theorem to justify certain results . These rigorous analytical methods contribute to the credibility and reliability of the experimental outcomes.

Moreover, the limitations section of the paper acknowledges potential challenges and areas for future research, such as the need for methods to generate diverse prompt templates and the consideration of prompt engineering . By addressing these limitations, the paper demonstrates a critical self-assessment of the research methodology and opens avenues for further investigation to strengthen the scientific hypotheses.

Overall, the combination of theoretical proofs, experimental analyses, references to prior works, and acknowledgment of limitations in the paper collectively provide robust support for the scientific hypotheses under investigation. The thoroughness and depth of the research methodology enhance the credibility and validity of the findings, contributing to the overall scientific rigor of the study.


What are the contributions of this paper?

The contributions of the paper are as follows:

  • The paper is supported by the National Science Foundation (NSF) under grants no. 2027737 and 2113373 .
  • It focuses on label-efficient model selection for text generation .
  • The paper discusses the benefits of dynamically generating new prompt candidates, as demonstrated by recent works .
  • It explores gradient-free, edit-based instruction search for prompting large language models .
  • The paper delves into the evaluation landscape considerations in benchmarks and frameworks for large language models (LLMs) .

What work can be continued in depth?

To delve deeper into the research on multi-prompt evaluation of Large Language Models (LLMs), one promising avenue for future work is the adaptation of the correctness model for bounded Yij values. Some LLM evaluation scenarios involve response variables that are bounded within the interval [0, 1], such as in AlpacaEval 2.0 and other frameworks like HELM and the Open LLM Leaderboard . Exploring different models for handling continuous Yij values, such as the Beta model or binarizing Yij as suggested by Polo et al. , could provide valuable insights into improving evaluation methodologies for LLMs.

Additionally, a critical area for further investigation is the development of methods for generating and diversifying multiple prompt templates. While existing research has proposed techniques for creating new prompts dynamically based on successful iterations , addressing the challenge of selecting an optimal set of prompt templates remains a key aspect to explore. Future studies could focus on refining prompt engineering strategies to enhance the effectiveness and reliability of LLM evaluations across various tasks and benchmarks.

Moreover, advancing the understanding of computing resources required for LLM evaluation is essential. Conducting experiments with different computational setups, exploring the impact of varying resources on evaluation outcomes, and optimizing resource allocation for fine-tuning BERT embeddings are areas that warrant further investigation to ensure efficient and scalable evaluation processes for LLMs.

In summary, future research directions in the field of multi-prompt evaluation of LLMs could involve:

  • Adapting correctness models for bounded Yij values in LLM evaluation scenarios .
  • Developing methods for generating and diversifying multiple prompt templates to enhance evaluation robustness .
  • Investigating computing resource requirements and optimization strategies for efficient LLM evaluation processes .

Introduction
Background
[Limited Benchmark Prompts Challenge]
[Growing Need for Efficient Evaluation Techniques]
Objective
[Introducing PromptEval: A Solution]
[Key Focus: Consistency and Efficiency]
Method
Data Collection
Item Response Theory (IRT) Application
[Estimating Performance Distributions]
[Practical Budget Considerations]
Data Preprocessing
[Prompt Template Selection and Standardization]
[Handling Diversity in Prompt Templates]
Evaluation Datasets
[MMLU: Multilingual Multitask Benchmark]
[BIG-bench Hard: Challenging Tasks]
[LMentry: Prompt-Response Evaluations]
Performance Estimation Techniques
PromptEval
[Leveraging Information Across Prompts]
[Estimating Errors and Quantiles]
PE-Rasch and PE-EmbFT Variants
[Improved Estimation Over Baselines]
Sensitivity Analysis
[Prompt Sensitivity in LLM Performance]
[Importance of Consistent Prompting]
Results and Applications
[Effectiveness of PromptEval in Practice]
[Real-World Implications for Model Selection and Development]
[Comparison with Traditional Evaluation Methods]
Conclusion
[Strengths and Limitations of PromptEval]
[Future Directions and Open Research Questions]
[Potential for Standardization in LLM Evaluation]
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What method does PromptEval employ to evaluate large language models?
How does PromptEval address the issue of limited benchmark prompts?
What theory is used in PromptEval to estimate performance distributions and quantiles?
Which datasets has PromptEval been demonstrated on for its effectiveness?

Efficient multi-prompt evaluation of LLMs

Felipe Maia Polo, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, Mikhail Yurochkin·May 27, 2024

Summary

PromptEval is a method introduced to efficiently evaluate large language models across diverse prompt templates, addressing the issue of limited benchmark prompts. It uses item response theory to estimate performance distributions and quantiles with practical budgets, providing a more robust measure of LLM capabilities. The technique is consistent and demonstrated on MMLU, BIG-bench Hard, and LMentry datasets, showing its effectiveness in estimating performance with limited evaluations. PromptEval estimates performance by leveraging information across prompts and examples, and its variants, such as PE-Rasch and PE-EmbFT, outperform baselines in estimating distribution and quantile errors. The study also highlights the importance of prompt sensitivity and the need for consistent templates in LLM evaluations.
Mind map
[Improved Estimation Over Baselines]
[Estimating Errors and Quantiles]
[Leveraging Information Across Prompts]
[LMentry: Prompt-Response Evaluations]
[BIG-bench Hard: Challenging Tasks]
[MMLU: Multilingual Multitask Benchmark]
[Practical Budget Considerations]
[Estimating Performance Distributions]
[Importance of Consistent Prompting]
[Prompt Sensitivity in LLM Performance]
PE-Rasch and PE-EmbFT Variants
PromptEval
Evaluation Datasets
Item Response Theory (IRT) Application
[Key Focus: Consistency and Efficiency]
[Introducing PromptEval: A Solution]
[Growing Need for Efficient Evaluation Techniques]
[Limited Benchmark Prompts Challenge]
[Potential for Standardization in LLM Evaluation]
[Future Directions and Open Research Questions]
[Strengths and Limitations of PromptEval]
[Comparison with Traditional Evaluation Methods]
[Real-World Implications for Model Selection and Development]
[Effectiveness of PromptEval in Practice]
Sensitivity Analysis
Performance Estimation Techniques
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Applications
Method
Introduction
Outline
Introduction
Background
[Limited Benchmark Prompts Challenge]
[Growing Need for Efficient Evaluation Techniques]
Objective
[Introducing PromptEval: A Solution]
[Key Focus: Consistency and Efficiency]
Method
Data Collection
Item Response Theory (IRT) Application
[Estimating Performance Distributions]
[Practical Budget Considerations]
Data Preprocessing
[Prompt Template Selection and Standardization]
[Handling Diversity in Prompt Templates]
Evaluation Datasets
[MMLU: Multilingual Multitask Benchmark]
[BIG-bench Hard: Challenging Tasks]
[LMentry: Prompt-Response Evaluations]
Performance Estimation Techniques
PromptEval
[Leveraging Information Across Prompts]
[Estimating Errors and Quantiles]
PE-Rasch and PE-EmbFT Variants
[Improved Estimation Over Baselines]
Sensitivity Analysis
[Prompt Sensitivity in LLM Performance]
[Importance of Consistent Prompting]
Results and Applications
[Effectiveness of PromptEval in Practice]
[Real-World Implications for Model Selection and Development]
[Comparison with Traditional Evaluation Methods]
Conclusion
[Strengths and Limitations of PromptEval]
[Future Directions and Open Research Questions]
[Potential for Standardization in LLM Evaluation]

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of robustly evaluating Language Model Models (LLMs) by proposing a method that minimizes dependence on a single prompt template and provides a holistic summary of performance across a broad set of templates . This problem is not entirely new, as previous benchmarks faced challenges in determining which single prompt to use, while the current challenge lies in selecting a set of prompt templates for evaluation . The paper emphasizes the importance of evolving prompt candidates dynamically to enhance evaluation methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that common evaluation methods for large language models (LLMs), which often rely on a single or limited number of prompt templates, may not adequately reflect the typical model's capabilities. This limitation can lead to unreliable and inconsistent rankings on LLM leaderboards, as different models may perform differently based on the specific prompt template used. The paper suggests the need for an evaluation framework that minimizes dependence on any single prompt template and provides a holistic summary of performance across a broad set of templates .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes several innovative ideas, methods, and models for efficient multi-prompt evaluation of LLMs:

  • Dynamic Prompt Generation: The paper suggests dynamically generating new prompt candidates to enhance evaluation. Prasad et al. introduce an evolutionary algorithm that creates new prompts based on previously successful ones, allowing for an evolving set of prompt candidates.
  • Holistic Evaluation Framework: It emphasizes the need for a holistic evaluation framework that minimizes reliance on a single prompt template. Weber et al. argue that traditional evaluation methods using limited prompt templates may not fully capture the capabilities of language models, leading to inconsistent rankings on leaderboards.
  • Multiple Prompt Templates: The study advocates for the use of multiple prompt templates to provide a comprehensive summary of model performance across various prompts. Mizrahi et al. highlight the importance of considering a diverse set of prompt variations to assess model performance accurately.
  • Adapting Correctness Models: The paper addresses situations where the response variable is bounded or continuous, suggesting adjustments to the correctness model for evaluation. It discusses potential fixes such as using the Beta model or binarizing the response variable .
  • Resource Utilization: The experiments conducted in the study were carried out using a virtual machine with 32 cores, and the results for each benchmark could be obtained within 3-6 hours .

These proposed ideas and methods aim to enhance the evaluation of LLMs by introducing dynamic prompt generation, advocating for a holistic evaluation approach, utilizing multiple prompt templates, adapting correctness models, and detailing the computational resources required for experimentation. The paper on efficient multi-prompt evaluation of LLMs introduces several novel characteristics and advantages compared to previous methods:

  • Dynamic Prompt Generation: The paper proposes dynamically generating new prompt candidates based on successful ones from earlier iterations, enhancing the adaptability of the evaluation process .
  • Holistic Evaluation Framework: It emphasizes the importance of a holistic evaluation approach that considers a diverse set of prompt templates to provide a comprehensive assessment of language models' capabilities .
  • Efficient Performance Distribution Computation: The study addresses the challenge of efficiently computing the performance distribution of LLMs over multiple prompt templates, aiming to streamline evaluations and reduce costs .
  • Adapting Correctness Models: The paper discusses adjustments to correctness models for bounded or continuous response variables, ensuring accurate evaluation under different scenarios .
  • Resource Utilization: The experiments conducted in the study were carried out using a virtual machine with 32 cores, providing insights into the computational resources required for efficient evaluation .

These characteristics highlight the paper's contributions in enhancing evaluation processes through dynamic prompt generation, holistic evaluation frameworks, efficient performance distribution computation, adapting correctness models, and considerations for resource utilization in LLM evaluations.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research works exist in the field, and there are noteworthy researchers who have contributed significantly to this topic. Some of the notable researchers mentioned in the context include Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal, Nils Reimers, Iryna Gurevych, Sidney Resnick, Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, and many others . These researchers have made valuable contributions to areas such as prompt-based learning, language model evaluation, and item response theory.

The key to the solution mentioned in the paper involves dynamically generating new prompt candidates. Researchers like Prasad et al. proposed an evolutionary algorithm that creates new prompts based on those that performed well in earlier iterations . This approach demonstrates the benefits of evolving prompt candidates to enhance the performance of large language models in various tasks. By adapting and generating new prompts dynamically, researchers aim to improve the efficiency and effectiveness of prompt-based learning and evaluation processes in the field of natural language processing.


How were the experiments in the paper designed?

The experiments in the paper were designed by conducting experiments using a virtual machine with 32 cores for each benchmark separately, with results obtainable within 3-6 hours . The training data consisted of concatenated prompting templates with all Example ID tokens, resulting in different dataset sizes for each benchmark . The experiments involved training on an iid split of half of the LLMs at a time and testing on the other half, with the training data split into an 80% training and 20% validation set . Additionally, a small grid search was performed over different hyperparameter settings, settling on specific setups such as using the Adam optimizer with an initial learning rate of 2e-5 and a weight decay of 1e-5, along with other parameters like batch size and learning rate adjustments .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is derived from three popular benchmarks: MMLU, BIG-bench Hard (BBH), and LMentry . The code used in the study is open source, as it is mentioned that the evaluation data will be released .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The paper includes detailed proofs and lemmas to support the theoretical framework of the research . Additionally, the references to prior works and related studies demonstrate a comprehensive understanding of the existing literature and build upon previous research findings.

The experiments conducted in the paper involve complex mathematical analyses and statistical evaluations, such as the use of the Dominated Convergence Theorem, Fubini’s Theorem, and the Lebesgue Dominated Convergence Theorem to justify certain results . These rigorous analytical methods contribute to the credibility and reliability of the experimental outcomes.

Moreover, the limitations section of the paper acknowledges potential challenges and areas for future research, such as the need for methods to generate diverse prompt templates and the consideration of prompt engineering . By addressing these limitations, the paper demonstrates a critical self-assessment of the research methodology and opens avenues for further investigation to strengthen the scientific hypotheses.

Overall, the combination of theoretical proofs, experimental analyses, references to prior works, and acknowledgment of limitations in the paper collectively provide robust support for the scientific hypotheses under investigation. The thoroughness and depth of the research methodology enhance the credibility and validity of the findings, contributing to the overall scientific rigor of the study.


What are the contributions of this paper?

The contributions of the paper are as follows:

  • The paper is supported by the National Science Foundation (NSF) under grants no. 2027737 and 2113373 .
  • It focuses on label-efficient model selection for text generation .
  • The paper discusses the benefits of dynamically generating new prompt candidates, as demonstrated by recent works .
  • It explores gradient-free, edit-based instruction search for prompting large language models .
  • The paper delves into the evaluation landscape considerations in benchmarks and frameworks for large language models (LLMs) .

What work can be continued in depth?

To delve deeper into the research on multi-prompt evaluation of Large Language Models (LLMs), one promising avenue for future work is the adaptation of the correctness model for bounded Yij values. Some LLM evaluation scenarios involve response variables that are bounded within the interval [0, 1], such as in AlpacaEval 2.0 and other frameworks like HELM and the Open LLM Leaderboard . Exploring different models for handling continuous Yij values, such as the Beta model or binarizing Yij as suggested by Polo et al. , could provide valuable insights into improving evaluation methodologies for LLMs.

Additionally, a critical area for further investigation is the development of methods for generating and diversifying multiple prompt templates. While existing research has proposed techniques for creating new prompts dynamically based on successful iterations , addressing the challenge of selecting an optimal set of prompt templates remains a key aspect to explore. Future studies could focus on refining prompt engineering strategies to enhance the effectiveness and reliability of LLM evaluations across various tasks and benchmarks.

Moreover, advancing the understanding of computing resources required for LLM evaluation is essential. Conducting experiments with different computational setups, exploring the impact of varying resources on evaluation outcomes, and optimizing resource allocation for fine-tuning BERT embeddings are areas that warrant further investigation to ensure efficient and scalable evaluation processes for LLMs.

In summary, future research directions in the field of multi-prompt evaluation of LLMs could involve:

  • Adapting correctness models for bounded Yij values in LLM evaluation scenarios .
  • Developing methods for generating and diversifying multiple prompt templates to enhance evaluation robustness .
  • Investigating computing resource requirements and optimization strategies for efficient LLM evaluation processes .
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