用简单方法提升大模型对模糊问题的理解能力
Do LLMs Understand Ambiguity in Text? A Case Study in Open-world Question Answering
- 不需训练，在标记层面直接处理文本歧义
- 实验表明该方法显著改善大模型问答准确率
- 适合关注模型可靠性与事实核查的研究者
自然语言中的歧义给用于开放域问答的大语言模型带来重大挑战。这些模型常因人类沟通的固有不确定性而产生误解、误传、幻觉和偏见，严重削弱其在事实核查、问答、特征提取和情感分析等任务中的应用能力。本文以开放域问答为案例，比较现成及少量样本提示的LLM表现，重点评估显式消歧策略的影响。结果表明，简单、无需训练、基于标记级别的消歧方法能有效提升模型在模糊问题上的表现。我们通过实证研究验证了这一发现，并讨论了关于大模型中歧义处理的最佳实践与更广泛影响。
原文摘要 · Abstract (English)
Ambiguity in natural language poses significant challenges to Large Language Models (LLMs) used for open-domain question answering. LLMs often struggle with the inherent uncertainties of human communication, leading to misinterpretations, miscommunications, hallucinations, and biased responses. This significantly weakens their ability to be used for tasks like fact-checking, question answering, feature extraction, and sentiment analysis. Using open-domain question answering as a test case, we compare off-the-shelf and few-shot LLM performance, focusing on measuring the impact of explicit disambiguation strategies. We demonstrate how simple, training-free, token-level disambiguation methods may be effectively used to improve LLM performance for ambiguous question answering tasks. We empirically show our findings and discuss best practices and broader impacts regarding ambiguity in LLMs.
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