Review of Research Progress on Question-Answering Techniques Based on Large Language Models
[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 retrieval-enhanced 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.
Large Language ModelsQ&A TechnologyVector RetrievalPrompt Engineering
万方数据
维普
