RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the optimization of prompts used in Large Language Models (LLMs) by proposing a new automatic prompt optimizer called REPROMPT. This optimizer is based on summarizing the interaction between LLM agents and feedback providers to enhance the performance of LLM agents by updating the prompts . The specific problem tackled by the paper is the need for efficient prompt engineering in LLM agents to improve their performance in reasoning tasks . While prompt optimization is not a new concept, the paper introduces a novel method, REPROMPT, to enhance the prompts used in LLM agents, focusing on specific reasoning tasks and leveraging dialogue history for prompt updates .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the scientific hypothesis related to optimizing prompts used in Large Language Models (LLMs) by proposing a new automatic prompt optimizer called REPROMPT . The focus is on summarizing the interaction between LLM agents and feedback providers to enhance the performance of LLMs through updated prompts . The experiments conducted in the paper demonstrate the benefits of an updated prompt for LLM agents in both 1-epoch and 5-epochs settings . The research emphasizes the importance of prompt optimization in improving the efficiency and effectiveness of LLM agents in various tasks .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents" proposes several innovative ideas, methods, and models in the field of optimizing prompts for Large Language Models (LLMs) . Here are the key contributions of the paper:
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Automatic Prompt Optimizer - REPROMPT: The paper introduces a new automatic prompt optimizer called REPROMPT, which is based on summarizing the interaction between LLM agents and feedback providers . This optimizer aims to improve the prompts used in LLM agents by providing specific instructions on how the current prompt can be enhanced .
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Gradient-Based Prompt Optimization: The proposed method utilizes "gradient-based" prompt optimization in LLM agents, focusing on optimizing prompts for reasoning tasks . This approach aims to improve the effectiveness of LLM agents by updating prompts in a step-by-step format .
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Summarization-Based Method: The paper suggests a summarization-based method to enhance prompts by identifying common prompt parts that can be improved, rather than scenario-specific prompts . This method aims to optimize prompts without overfitting on specific data points .
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Improved Results in LLM Benchmarks: Through experiments, the paper demonstrates that updated prompts can enhance the performance of LLM agents in various benchmarks without the need for fine-tuning the models .
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Planning in the Prompt Phase: The proposed method focuses on planning in the prompt phase, which is beneficial for specific tasks where LLM agents require different procedures in various scenarios . It is particularly effective for tasks like high-school geometry problems .
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Iterative Prompt Refinement: The paper introduces an iterative prompt refinement process, similar to training machine learning models through epochs, to converge on an optimized prompt that improves the generated results . This iterative process involves proposing potential solutions, analyzing them, and updating the prompt accordingly .
In summary, the paper introduces REPROMPT as an automatic prompt optimizer that leverages gradient-based prompt optimization and summarization-based methods to enhance prompts for LLM agents, leading to improved performance in various benchmarks without the need for extensive model fine-tuning . The paper "RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents" introduces several characteristics and advantages compared to previous methods in prompt optimization for Large Language Models (LLMs) .
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Gradient-Based Prompt Optimization: The proposed method utilizes "gradient-based" prompt optimization, focusing on optimizing prompts for reasoning tasks . This approach allows for fine-tuning the prompts based on the interaction history between LLM agents and feedback providers, leading to improved performance in various reasoning tasks.
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Summarization-Based Method: The paper suggests a summarization-based method to enhance prompts by identifying common prompt parts that can be improved, rather than scenario-specific prompts . This method ensures that the prompts are optimized step-by-step, providing specific instructions on how to enhance the current prompt effectively.
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Improved Performance in Benchmarks: Through experiments, the paper demonstrates that updated prompts can enhance the performance of LLM agents in various benchmarks without the need for extensive model fine-tuning . This indicates that the proposed method can efficiently optimize prompts and improve the overall performance of LLM agents in different reasoning tasks.
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Generalizability and Efficiency: The proposed method shows generalizability in different reasoning tasks, such as PDDL generation and travel planning, by achieving a higher first-round success rate . This highlights the efficiency of the approach in optimizing prompts for diverse domains without the need for additional human annotations.
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Reduced Dependency on Human Expertise: The method reduces the dependency on domain experts by providing accurate but expensive feedback, allowing for prompt optimization with minimal human intervention . This streamlines the prompt optimization process and accelerates the translation of prompts for improved LLM performance.
In conclusion, the characteristics of the proposed method, including gradient-based prompt optimization, summarization-based enhancement, improved benchmark performance, generalizability, and reduced dependency on human expertise, offer significant advantages compared to previous prompt optimization methods for LLM agents . These features contribute to more efficient and effective prompt engineering, leading to enhanced performance in various reasoning tasks without the need for extensive manual intervention.
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 papers exist in the field, with notable researchers contributing to this topic. Some of the noteworthy researchers mentioned in the context are Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, Karthik Narasimhan, Nan Du, Karthik R. Narasimhan, Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, Michael Zeng, Guanghui Qin, Jason Eisner, among others . The key solution mentioned in the paper is the development of an automatic prompt optimizer called REPROMPT, which focuses on optimizing prompts used in Large Language Models (LLMs) by summarizing the interaction between LLM agents and feedback providers .
How were the experiments in the paper designed?
The experiments in the paper were designed to showcase the effectiveness of the proposed method, REPROMPT, in optimizing prompts for Large Language Models (LLMs) in various domains . The experiments focused on Planning Domain Definition Language (PDDL) generation and travel planning to demonstrate how the method can achieve a higher first-round success rate . The goal was to show that by using updated prompts, the results in multiple LLM agent benchmarks could be improved without the need to fine-tune the LLM models . The experiment settings included using the same stopping criteria and hyperparameter settings for each domain as in the original environments, with a temperature of 0 and a seed of 42, and testing the results on GPT-4-turbo-1106-preview due to its strong general capability .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the context of the paper "RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents" is not explicitly mentioned in the provided excerpts. However, the paper discusses the evaluation phase and the comparison of success rates, indicating that the dataset used for quantitative evaluation is not specified .
Regarding the code being open source, at the time of submission of the paper, the evaluation phase was missing in the official Github repository, which hindered the ability to compare success rates in a fair manner. This suggests that the code for the evaluation phase may not have been openly available or accessible for comparison .
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 needed verification. The paper introduces a new automatic prompt optimizer, REPROMPT, which optimizes prompts used in Large Language Models (LLMs) . The experiments conducted show that LLM agents benefit from an updated prompt in both 1-epoch and 5-epochs settings . The results demonstrate improved success rates with the optimized prompts, indicating that the hypotheses regarding the effectiveness of optimizing prompts for LLM agents are well-supported by the experimental outcomes .
Furthermore, the paper discusses the limitations of the proposed method, highlighting areas where improvements can be made for future research . By acknowledging the constraints and potential challenges of the REPROMPT method, the paper provides a comprehensive analysis that contributes to the scientific rigor of the study . This critical evaluation of the limitations ensures a balanced perspective on the effectiveness and applicability of the proposed prompt optimization approach .
In conclusion, the experiments and results presented in the paper offer strong empirical evidence to support the scientific hypotheses related to optimizing prompts for LLM agents. The findings demonstrate the positive impact of updated prompts on the performance of LLM agents, while also acknowledging the limitations and areas for future research improvement, thereby enhancing the credibility and validity of the scientific investigation .
What are the contributions of this paper?
The contributions of the paper "RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents" include:
- Proposing the use of "gradient-based" prompt optimization in Large Language Models (LLM) agents .
- Introducing a summarization-based method to provide specific instructions on improving the current prompt and suggesting a novel guideline for optimizing the prompt in a step-by-step format .
- Demonstrating that by using updated prompts, the results in multiple LLM agent benchmarks can be improved without the need for fine-tuning the LLM models .
What work can be continued in depth?
Further work in this area can delve deeper into several aspects:
- Exploring Search-Based Mechanisms: Future research could focus on incorporating a search-based mechanism to identify and rectify mistakes made by the prompt generator, potentially enhancing the overall results .
- Adapting to Different Domains: Investigating how the proposed method, REPROMPT, can adapt to various domains that require distinct procedures, such as LLM agents designed for specific tasks like solving high-school geometry problems .
- Enhancing Prompt Optimization: Research could aim to refine the prompt optimization process by considering the removal of redundant or ineffective steps in the prompt, which could contribute to better outcomes .
- Addressing General vs. Specific Tasks: Further exploration is needed to determine the effectiveness of the proposed method for LLM agents intended for general domains with diverse requirements compared to those tailored for specific tasks .