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://github.com/DCDmllm/Meta-Reflection

Leveraging Large Language Models (LLMs) to build domain-specific conversational agents, especially for e-commerce customer service chatbots, is a growing focus. While existing methods enhance dialogue performance by extracting core patterns from dialogue data and integrating them into models, two key challenges persist: (1) heavy reliance on human experts for dialogue strategy induction, and (2) LLM-based automatic extraction often focuses on summarizing specific behaviors, neglecting the underlying thought processes behind strategy selection. In this paper, we present ChatMap, which focuses on enhancing customer service chatbots by mining thought processes using a Multi-Agent aPproach. Specifically, the process begins by extracting customer requests and solutions from a raw dialogue dataset, followed by clustering similar requests, analyzing the thought processes behind solutions, and refining service thoughts. Through a quality inspection and reflection mechanism, the final service thought dataset is generated, helping chatbots provide more appropriate responses. Offline experimental results show that ChatMap performs comparably to manually annotated thought processes and significantly outperforms other baselines, demonstrating its ability to automate human annotation and enhance dialogue capabilities through strategic understanding. Online A/B tests on Taobao, a popular e-commerce platform in China reveal that ChatMap can better improve customer satisfaction and address customer requests from a business perspective.