Meta-Reflection: A Feedback-Free Reflection Learning Framework

ACL ARR 2025 February Submission2680 Authors

Abstract:

Despite the remarkable capabilities of large language models (LLMs) in natural language understanding and reasoning, they often display undesirable behaviors, such as generating hallucinations and unfaithful reasoning. A prevalent strategy to mitigate these issues is the use of reflection, which refines responses through an iterative process. However, while promising, reflection heavily relies on high-quality external feedback and requires iterative multi-agent inference processes, thus hindering its practical application. In this paper, we propose
Meta-Reflection, a novel feedback-free reflection mechanism that necessitates only a single inference pass without external feedback. Motivated by the human ability to remember and retrieve reflections from past experiences when encountering similar problems, Meta-Reflection integrates reflective insights into a codebook, allowing the historical insights to be stored, retrieved, and used to guide LLMs in problem-solving. To thoroughly investigate and evaluate the practicality of Meta-Reflection in real-world scenarios, we introduce an industrial e-commerce benchmark named E-commerce Customer Intent Detection. Extensive experiments conducted on both public datasets and the ECID benchmark highlight the effectiveness and efficiency of our proposed approach. Project is available at https://anonymous.4open.science/r/Meta-Reflection-62F5/

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: fine-tuning, embodied agents, code generation and understanding, mathematical NLP, NLP datasets

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English, Chinese

Submission Number: 2680