针对含错误前提的问题，提出可解释的识别与回答方法。
Identifying and Answering Questions with False Assumptions: An Interpretable Approach
- 通过检索外部证据识别问题中的错误假设。
- 结合证据生成并验证原子假设，提升答案准确性。
- 方法可解释，适合需可信推理的场景。
人们常提出带有错误前提的问题，这类问题无标准答案。回答此类问题需先识别错误前提。大型语言模型因幻觉倾向，常给出误导性回答。本文聚焦多个领域中含错误前提问题的识别与回答。首先探究该问题是否可归约为事实验证；随后提出一种利用外部证据缓解幻觉的方法。五种大模型的实验表明：（1）引入检索到的证据有益于提升性能；（2）生成并验证原子假设带来更大改进，并通过定位错误前提实现可解释的回答。
原文摘要 · Abstract (English)
People often ask questions with false assumptions, a type of question that does not have regular answers. Answering such questions requires first identifying the false assumptions. Large Language Models (LLMs) often generate misleading answers to these questions because of hallucinations. In this paper, we focus on identifying and answering questions with false assumptions in several domains. We first investigate whether the problem reduces to fact verification. Then, we present an approach leveraging external evidence to mitigate hallucinations. Experiments with five LLMs demonstrate that (1) incorporating retrieved evidence is beneficial and (2) generating and validating atomic assumptions yields more improvements and provides an interpretable answer by pinpointing the false assumptions.
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