When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2402.10890v2)

Abstract: In this paper, we examine how LLMs solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using these two methods or a simpler method, re-ranking. Experiments on two tasks, text-to-SQL parsing and mathematical reasoning, show that: (1) advanced planning methods demand discriminators with at least 90% accuracy to achieve significant improvements over re-ranking; (2) current LLMs' discrimination abilities have not met the needs of advanced planning methods to achieve such improvements; (3) with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency. For example, compared to the other two methods, tree search is at least 10--20 times slower but leads to negligible performance gains, which hinders its real-world applications. Code and data are available at https://github.com/OSU-NLP-Group/LLM-planning-eval.

• The paper finds that advanced planning methods notably boost LLM performance only when the discriminator achieves around 90% accuracy.
• It evaluates tree search and iterative correction over re-ranking using tasks like text-to-SQL parsing and mathematical reasoning to balance efficiency and accuracy.
• The study underscores that improving discriminator quality via environmental feedback is essential to fully leverage advanced planning techniques in LLMs.

Examining the Efficacy of Tree Search and Iterative Correction in LLM Planning Based on Discriminator Accuracy

Introduction

The integration of planning methods with LLMs for solving multi-step problems embodies a significant stride towards enhancing artificial intelligence capabilities. The paper "When is Tree Search Useful for LLM Planning? It Depends on the Discriminator" embarks on an evaluative journey to understand the potency of advanced planning methods—iterative correction and tree search—over a simpler approach, namely re-ranking. It pivots around the role of discriminator accuracy in determining the effectiveness of these planning strategies, using tasks like text-to-SQL parsing and mathematical reasoning as the test bed.

Analysis of Planning Methods and Discriminator Accuracy

Progressive Planning Methods

The planning method employed by an agent significantly influences its problem-solving trajectory. This paper meticulously assesses three planning methods: re-ranking, iterative correction, and tree search, to explore their practical utility and efficiency when integrated with LLMs. The empirical findings reveal a nuanced landscape where the superiority of advanced planning methods over re-ranking is intrinsically tied to the accuracy of the discriminator, highlighting a pivotal but challenging threshold of at least 90% accuracy needed for notable performance gains.

Discriminator Criticality

The discriminator emerges as a cornerstone in the mechanism of LLM-based planning methods, its accuracy being paramount for substantial improvement over simpler methods like re-ranking. The investigation into LLMs' discrimination abilities sheds light on both the potential and limitations of current models. Despite improvements through environmental feedback—augmenting discrimination accuracy by substantial margins—the paper underscores a critical gap: existing LLMs, even when enhanced, barely meet the advanced planning methods' requisites.

Efficiency vs. Accuracy Trade-off

The discourse on the interplay between advanced planning methods and LLM-based discriminators brings to fore an intrinsic trade-off between accuracy and efficiency. Advanced planning methods, although theoretically potent, grapple with practical constraints. For instance, tree search, despite its methodological sophistication, demonstrates negligible performance gains coupled with efficiency drawbacks, a revelation that poses significant implications for real-world applications.

Theoretical and Practical Implications

The Role of Discriminator Quality

The dissection of discriminators' quality within the planning framework underscores a critical finding: high-quality discriminators are indispensable for unleashing the full potential of advanced planning methods. This insight not only illuminates the path for future research endeavors aimed at enhancing discriminators' accuracy but also stipulates a significant theoretical pivot—discrimination accuracy as a threshold criterion for the efficacy of planning methods in LLMs.

Future Prospects in AI Development

The paper anticipates the evolution of discrimination capabilities as a burgeoning domain of interest, advocating for a research trajectory focused on elevating discriminator accuracy. Such advancements are envisaged to recalibrate the efficiency-accuracy scales favoring advanced planning methods, thus broadening the horizons for deploying LLMs in complex, real-world problem solving. The proposed analytical framework for evaluating planning methods in tandem with discriminators' performance paves the way for a structured exploration of this future direction.

Conclusion

This paper delineates the intricate relationship between discriminator accuracy and the effectiveness of planning methods in LLMs, spotlighting discriminator quality as a pivotal factor. It anchors a significant benchmark for future innovations aimed at refining LLM-based discriminators, with the ultimate goal of optimizing the planning methodologies within artificial intelligence paradigms. The insights gleaned from this paper not only contribute to the academic discourse around LLM planning but also echo potential advancements in AI problem-solving capabilities, laying a foundation for future explorations in intelligent behavior modeling.