Identifying and Answering Questions with False Assumptions: An Interpretable Approach (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) generate misleading answers because of hallucinations . despite their capabilities, LLMs suffer from hallucinisms, which leads to unfaithful answers . |
| Approach: | They propose a method to identify and answer questions with false assumptions . they first investigate whether the problem reduces to fact verification . then, they leverage external evidence to mitigate hallucinations . |
| Outcome: | The proposed approach reduces the problem to fact verification and provides interpretable answers by pinpointing the false assumptions. |
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