Key Points
- The paper investigates how large language models (LLMs) solve multi-step problems under a language agent framework with a generator, a discriminator, and a planning method.
- The practical utility of advanced planning methods, such as iterative correction and tree search, is examined, and it is found that discriminators with at least 90% accuracy are needed to achieve significant improvements over re-ranking.
- Current LLMs' discrimination abilities have not met the needs of advanced planning methods to achieve such improvements, and with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency.
- Planning plays a crucial role in intelligent behaviors of human and AI agents, and various methods have been proposed to build agents that can plan efficiently and accurately.
- Large language models (LLMs) solve multi-step tasks by searching for possible next actions, predicting their expected outcomes, and finding an action sequence to achieve the best expected outcome.
- The paper presents a comprehensive analysis of advanced planning methods, such as tree search, in comparison with simpler methods (e.g., re-ranking), and systematically investigates the impact of discrimination accuracy on language agents' performance.
- Discrimination accuracy closely correlates with the performance of agents on all datasets, and advanced planning methods demand highly accurate discriminators (≥ 90% accuracy) to achieve decent improvements over re-ranking.
- LLM-based discriminators have not yet met the needs of advanced planning methods, and future research should investigate the development of more accurate discrimination models for language agents.
- The paper concludes by highlighting the impact of the generator on planning methods and the need for future research to examine the generator's impact on planning methods.
Summary
Advanced Planning Methods and Discrimination Accuracy
This paper examines the practical utility of advanced planning methods in large language models (LLMs) for solving multi-step problems, particularly in the context of a generator-discriminator framework. The study focuses on the impact of discrimination accuracy on agent performance and analyzes two tasks, namely text-to-SQL parsing and mathematical reasoning. The research finds that advanced planning methods, such as iterative correction and tree search, require discriminators with at least 90% accuracy to achieve significant improvements over simpler methods like re-ranking. However, the current LLMs' discrimination abilities have not fully met these needs. Additionally, the study shows that advanced planning methods may struggle to balance accuracy and efficiency when using LLM-based discriminators.
Impact of Discrimination Abilities on Language Agents
The experiments demonstrate that discrimination accuracy significantly affects the overall performance of language agents using different planning methods. The study also evaluates the discrimination abilities of LLMs and their impact on planning methods, showing that LLM-based discriminators may not accurately assess language agents' actions in practical settings. Moreover, the research proposes improvements to enhance LLMs' discrimination capability and provides insights into the balance between accuracy and efficiency in advanced planning methods. The findings highlight the importance of more accurate discrimination models for language agents, and future research is encouraged to thoroughly evaluate language agents with various practical, non-oracle discriminators.
Influence of Generators on Planning Methods
The paper also addresses the potential impact of generators on planning methods and acknowledges the need for further research on the generator's influence. Overall, the study provides comprehensive insights into the practical utility of advanced planning methods in LLMs and emphasizes the significance of accurate discrimination models for enhancing the performance of language agents in real-world applications.
Reference: https://arxiv.org/abs/2402.10890