RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents

• URL: http://arxiv.org/abs/2406.11132v2
• Date: Thu, 13 Feb 2025 21:38:42 GMT
• Title: RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
• Authors: Weizhe Chen, Sven Koenig, Bistra Dilkina,
• Abstract summary: We propose a novel method, textscRePrompt, which does agradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents. By leveraging intermediate feedback, textscRePrompt can optimize the prompt without the need for a final solution checker.
• Score: 27.807695570974644
• License:
• Abstract: In the past year, large language models (LLMs) have had remarkable success in domains outside the traditional natural language processing, and their capacity is further expanded into the so-called LLM agents when connected with external tools. In all domains, the prompt to the LLMs has been shown to make a big difference in what the LLM would generate and thus affect the performance of the LLM agents. Therefore, automatic prompt engineering (APE) has become an important question for many researchers and users of LLMs. However, previous works in APE rely on a final checker to evaluate the performance of the given prompt -- a requirement that is hard to meet in the case of LLM agents, where intermediate feedback is easier to obtain, and the final evaluation could be expensive, inaccurate, or even missing. In this paper, we propose a novel method, \textsc{RePrompt}, which does a ``gradient descent"-like approach to optimize the step-by-step instructions in the prompts given to LLM agents, based on the chat history obtained from interactions and reflections with LLM agents. By leveraging intermediate feedback, \textsc{RePrompt} can optimize the prompt without the need for a final solution checker. We evaluate our approach on PDDL generation, TravelPlanner, and Meeting Planning to show that our method could generally improve performance for different reasoning tasks.

Related papers

• LLM-AutoDiff: Auto-Differentiate Any LLM Workflow [58.56731133392544]
  We introduce LLM-AutoDiff: a novel framework for Automatic Prompt Engineering (APE) LLMs-AutoDiff treats each textual input as a trainable parameter and uses a frozen backward engine to generate feedback-akin to textual gradients. It consistently outperforms existing textual gradient baselines in both accuracy and training cost.
  arXiv  Detail & Related papers  (2025-01-28T03:18:48Z)
• Dynamic Ensemble Reasoning for LLM Experts [35.774197263383996]
  We propose a Dynamic Ensemble Reasoning paradigm, called DER, to integrate the strengths of multiple LLM experts conditioned on dynamic inputs. Our method uses fewer computational resources to achieve better performance compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-12-10T12:05:56Z)
• LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models [9.566432486156335]
  Large Language Models for Post-Ranking in search engine (LLM4PR) We introduce a novel paradigm named Large Language Models for Post-Ranking in search engine (LLM4PR)
  arXiv  Detail & Related papers  (2024-11-02T08:36:16Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization [19.200989737492595]
  Large language models (LLMs) have shown great progress in responding to user questions. The quality of LLM outputs heavily depends on the prompt design, where a good prompt might enable the LLM to answer a very challenging question correctly. We propose a hierarchy of LLMs, first constructing a prompt with precise instructions and accurate wording in a hierarchical manner, and then using this prompt to generate the final answer to the user query.
  arXiv  Detail & Related papers  (2024-05-30T17:05:45Z)
• One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models [67.49462724595445]
  Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) We propose a novel method that involves learning scalable and pluggable virtual tokens for RAG.
  arXiv  Detail & Related papers  (2024-05-30T03:44:54Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• A Survey on Prompting Techniques in LLMs [0.0]
  Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. We identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.
  arXiv  Detail & Related papers  (2023-11-28T17:56:34Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations [53.76682562935373]
  We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools. InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
  arXiv  Detail & Related papers  (2023-08-31T07:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.