基于大语言模型的问答技术研究进展综述

Review of Research Progress on Question-Answering Techniques Based on Large Language Models

【目的】全面回顾和概述基于大语言模型的问答技术发展现状、机制原理以及应用趋势。【文献范围】选取与基于大语言模型的问答技术相关的73篇文献。【方法】系统梳理大语言模型的发展现状、参数高效微调策略,分别从面向简单问题的检索增强生成问答推理以及面向复杂问题的提示工程问题推理两方面,深入解析各技术的原理机制、应用价值与存在问题。通过定性分析,全面概述基于大语言模型的问答技术研究进展,并提出未来研究方向。【结果】开源预训练大语言模型不断涌现,高效微调策略可显著提升模型垂直领域适配性。借助文本嵌入与近似最近邻检索技术,检索增强生成技术可有效提升问答可解释性与可信度。借助精心构造的提示工程,可大幅拓展大语言模型的复杂问题推理能力。【局限】大语言模型相关研究发展迅速,调研工作未全面覆盖。【结论】基于大语言模型的问答技术在语义表示、复杂推理等多个方面均取得显著进展,融合外部知识的检索增强生成技术与提示工程技术是当前大语言模型领域的主要研究热点,未来研究工作可在生成内容可控、可信等方面展开深入探索。

[Objective]This paper aims to comprehensively review and summarize the current development status,mechanism principles,and application trends of question-answering techniques based on large language models.[Coverage]We retrieved a total of 73 relevant papers.[Methods]The study systematically reviews the development status of large language models and efficient parameter fine-tuning strategies.It analyzes the principles,mechanisms,application value,and existing issues of various techniques.It focuses on retrievalenhanced generation question-answering inference for simple questions and prompt engineering question inference for complex questions.Through qualitative analysis,the research progress of question-answering techniques based on large language models is comprehensively summarized,and future research directions are proposed.[Results]Open-sourced pre-trained large language models continue to emerge,and efficient fine-tuning strategies can significantly improve model adaptability in vertical domains.Retrieval-augmented generation techniques,aided by text embeddings and approximate nearest neighbor retrieval technology,effectively enhance the interpretability and credibility of question-answering.With carefully crafted prompt engineering,the inference capabilities of large models for complex questions can be significantly expanded.[Limitations]The rapid development of research related to large models may result in incomplete coverage of relevant survey work.[Conclusions]Question-answering techniques based on large language models have made remarkable progress in semantic representation,complex reasoning,and other aspects.Retrieval-enhanced generation techniques and prompt engineering,which integrate external knowledge,are the main research hotspots in large models.Future research may focus on exploring aspects such as controllable and credible content generation.