Meta-Reflection: A Feedback-Free Reflection Learning Framework

TL;DR

Meta-Reflection introduces a feedback-free reflection mechanism for large language models, enabling improved reasoning and reduced hallucinations through a single inference pass by leveraging stored past insights.

Contribution

It proposes a novel feedback-free reflection framework that uses a codebook to store and retrieve insights, eliminating the need for external feedback and multiple inference iterations.

Findings

Effective in reducing hallucinations and unfaithful reasoning.

Achieves comparable or better performance with fewer inference passes.

Validated on public datasets and an industrial e-commerce benchmark.

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 (ECID). Extensive experiments conducted on both public datasets and the ECID benchmark highlight the effectiveness and efficiency of our proposed approach.