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.

翻译：尽管大型语言模型（LLM）在自然语言理解和推理方面展现出卓越能力，但它们常常表现出不良行为，例如产生幻觉和不忠实的推理。缓解这些问题的一种普遍策略是使用反思，即通过迭代过程优化响应。然而，尽管前景广阔，反思严重依赖于高质量的外部反馈，并且需要迭代的多智能体推理过程，从而阻碍了其实际应用。在本文中，我们提出元反思，一种新颖的无反馈反思机制，仅需单次推理过程且无需外部反馈。受人类在遇到类似问题时能够记忆并检索过往经验中的反思这一能力启发，元反思将反思性见解整合到一个码本中，使得历史见解能够被存储、检索并用于指导LLM解决问题。为了深入研究和评估元反思在现实场景中的实用性，我们引入了一个名为电子商务客户意图检测（ECID）的工业级电子商务基准。在公共数据集和ECID基准上进行的大量实验突显了我们所提出方法的有效性和效率。