Arizona State University Researchers Evaluate ReAct Prompts: The Role of Example Similarity in Improving Reasoning of Large Language Models

Large language models (LLM) have advanced rapidly, especially in natural language processing (NLP) and natural language understanding (NLU). These models excel in text generation, summaries, translation and answering questions. With these capabilities, researchers are interested in exploring their potential in tasks that require reasoning and planning. This study evaluates the effectiveness of specific stimulation techniques in improving LLMs' decision-making skills in complex sequential tasks.

A major challenge in leveraging LLMs for reasoning tasks is determining whether the improvements are genuine or superficial. The ReAct stimulation method, which integrates lines of reasoning with the execution of actions, aims to improve LLM performance in sequential decision making. However, there is an ongoing debate about whether these improvements are due to true reasoning abilities or simply pattern recognition based on the input examples. This study aims to analyze these claims and provide a clearer understanding of the factors that influence LLM performance.

Existing methods for improving LLM performance on reasoning tasks include various forms of rapid engineering. Techniques such as Chain of Thought (CoT) and ReAct guide LLMs through complex tasks by incorporating structured reasoning or instructions within the prompts. These methods are designed to make LLMs simulate a step-by-step problem-solving process, which is believed to help in tasks that require planning and logical progression.

The Arizona State University research team presented a comprehensive analysis to evaluate the claims of the ReAct framework. The ReAct method claims that interweaving reasoning traces with actions improves the decision-making capabilities of LLMs. The researchers conducted experiments using different models, including GPT-3.5-turbo, GPT-3.5-instruct, GPT-4 and Claude-Opus, within a simulated environment known as AlfWorld. By systematically varying the input cues, their goal was to identify the true source of performance improvements attributed to the ReAct method.

In their detailed analysis, the researchers introduced several variations to the ReAct prompts to test different aspects of the method. They examined the importance of interweaving reasoning traces with actions, the type and structure of guidance provided, and the similarity between example and inquiry tasks. Their findings were revealing. LLM performance was minimally influenced by the interweaving of reasoning traces with action execution. Instead, the critical factor was the similarity between the input examples and the queries, suggesting that the improvements were due to pattern matching rather than increased reasoning ability.

The experiments yielded quantitative results that highlighted the limitations of the ReAct framework. For example, GPT-3.5-turbo's success rate on six different tasks in AlfWorld was 27.6% with basic ReAct prompts, but improved to 46.6% when using example-based CoT prompts. Similarly, GPT-4 performance decreased significantly when the similarity between the example and query tasks was reduced, highlighting the fragility of the method. These results indicate that while ReAct may appear effective, its success depends largely on specific examples of indications.

A notable finding was that providing irrelevant guidance or placebo did not significantly degrade performance. For example, using a weaker or placebo guide, when the text did not provide relevant information, showed comparable results to a strong trace-based reasoning guide. This challenges the assumption that the content of the reasoning trace is crucial for LLM performance. Instead, success arises from the similarity between examples and tasks rather than from the inherent reasoning abilities of LLMs.

Research Overview

In conclusion, this study challenges the claims of the ReAct framework by demonstrating that its perceived benefits are primarily due to the similarity between example tasks and query tasks. The need for instance-specific examples to achieve high performance raises scalability issues for broader applications. The findings emphasize the importance of closely evaluating rapid engineering methods and their putative abilities to improve LLM performance on reasoning and planning tasks.

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