梳理大模型时代对话问答中的歧义问题与解决方法
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey
- 分类大模型驱动的歧义消解技术及其适用场景
- 对比不同方法在准确率与鲁棒性上的表现差异
- 适合关注智能对话系统可靠性研究者阅读
歧义仍是自然语言处理中的根本挑战，源于人类语言的复杂性和灵活性。随着大语言模型（LLMs）能力扩展，其在对话问答（CQA）中的应用使歧义问题更加突出。本文探讨了语言驱动系统中歧义的定义、形式及其影响，明确定义关键术语，系统分类由大模型支持的歧义消解方法，并对比分析其优劣。同时，梳理公开可用的数据集，评估其在歧义检测与消解技术评测中的价值。最后，识别当前开放问题，特别是在代理（agentic）环境下的研究方向，提出未来可探索的领域。通过全面综述大模型背景下歧义与消解的研究进展，旨在推动更稳健可靠的基于大模型系统的建设。
原文摘要 · Abstract (English)
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language Models (LLMs), addressing ambiguity has become even more critical due to their expanded capabilities and applications. In the context of Conversational Question Answering (CQA), this paper explores the definition, forms, and implications of ambiguity for language driven systems, particularly in the context of LLMs. We define key terms and concepts, categorize various disambiguation approaches enabled by LLMs, and provide a comparative analysis of their advantages and disadvantages. We also explore publicly available datasets for benchmarking ambiguity detection and resolution techniques and highlight their relevance for ongoing research. Finally, we identify open problems and future research directions, especially in agentic settings, proposing areas for further investigation. By offering a comprehensive review of current research on ambiguities and disambiguation with LLMs, we aim to contribute to the development of more robust and reliable LLM-based systems.
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