Mitigating Language Model Hallucination with Interactive Question-Knowledge Alignment

Shuo Zhang, Liangming Pan, Junzhou Zhao, William Yang Wang

Despite the remarkable recent advances in language models, they still struggle with the hallucination problem and can generate misleading and unsupported responses. A common approach to mitigate the hallucination issue is retrieving and incorporating supporting evidence from a knowledge base. However, user questions usually do not align well with the stored knowledge, as they are unaware of the information available before asking questions. This misalignment can limit the language model's ability to locate and utilize the knowledge, potentially forcing it to hallucinate by ignoring or overriding the retrieved evidence. To address this issue, we introduce MixAlign, a framework that interacts with both the user and the knowledge base to obtain and integrate clarifications on how the user question relates to the stored information. MixAlign employs a language model to achieve automatic question-knowledge alignment and, if necessary, further enhances this alignment through human user clarifications. Experimental results demonstrate significant improvements over state-of-the-art methods, showcasing the effectiveness of MixAlign in mitigating language model hallucination.

Knowledge Graph



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