Do Think Tags Really Help LLMs Plan? A Critical Evaluation of ReAct-Style Prompting
Keywords: Large Language Models, ReAct, Sequential Decision-Making
TL;DR: We study ReAct for sequential decision-making problems, and contrary to the original claims, show that the reason for improvement stems from high exemplar similarity with the query problem and not due to the generated & interleaved reasoning traces.
Abstract: The reasoning abilities of Large Language Models (LLMs) remain a topic of debate, which are critically tested in sequential decision-making problems. ReAct, a recently popular method has gained popularity for claiming to enhance LLM reasoning abilities while directly prompting them by $``\textit{interleaving reasoning trace with action execution}"$ in text-based planning domains such as AlfWorld and WebShop. However, given the different components of ReAct-style prompting, it remains unclear what the source of improvement in LLM performance is. In this paper, we critically examine the claims of ReAct-style prompting for sequential decision-making problems. By introducing systematic variations to the input prompt, we perform a sensitivity analysis along the original claims of ReAct. Contrary to these claims and common use-cases that utilize ReAct-style prompting, we find that the performance is minimally influenced by the interleaved reasoning trace or by the content of these generated reasoning traces. Instead, the performance of LLMs is primarily driven by the unreasonably high degree of similarity between input example tasks and queries, implicitly forcing the prompt designer to provide instance-specific examples which significantly increases the cognitive burden on the human. Our empirical results, on the same suite of domains as ReAct, show that the perceived reasoning abilities of LLMs stem from the exemplar-query similarity and approximate retrieval rather than any inherent reasoning abilities.
Submission Number: 93
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