Knowledge Verification to Nip Hallucination in the Bud
This addresses the issue of factual inaccuracies in LLM outputs for users relying on accurate information, representing an incremental improvement in alignment methods.
The paper tackles the problem of hallucinations in large language models by proposing Knowledge Consistent Alignment (KCA), which verifies and minimizes inconsistencies between external knowledge in alignment data and intrinsic model knowledge, resulting in reduced hallucinations across six benchmarks.
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. In this paper, we demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. Specifically, we propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-aligned LLM to automatically formulate assessments based on external knowledge to evaluate the knowledge boundaries of foundation LLMs. To address knowledge inconsistencies in the alignment data, KCA implements several specific strategies to deal with these data instances. We demonstrate the superior efficacy of KCA in reducing hallucinations across six benchmarks, utilizing foundation LLMs of varying backbones and scales. This confirms the effectiveness of mitigating hallucinations by reducing knowledge inconsistency. Our code, model weights, and data are openly accessible at \url{https://github.com/fanqiwan/KCA}.