Knowledge Verification to Nip Hallucination in the Bud

TL;DR

This paper introduces Knowledge Consistent Alignment (KCA), a method that reduces hallucinations in large language models by verifying and minimizing inconsistencies between external knowledge and the models' internal knowledge, showing improved results across multiple benchmarks.

Contribution

The paper proposes KCA, a novel approach that employs a well-aligned LLM to assess and reduce knowledge inconsistencies, effectively mitigating hallucinations in foundation LLMs.

Findings

KCA significantly reduces hallucinations across six benchmarks.

KCA is effective for various backbone LLMs and scales.

Open-source code and data are provided for reproducibility.

Abstract

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}.