Learning from Hallucinations: Mitigating Hallucinations in LLMs via Internal Representation Intervention

AbstractLarge language models (LLMs) sometimes hallucinate facts. Recent studies have shown that use of non-factual LLMs (anti-expert) have the potential to improve the factuality of the base LLM. Anti-expert methods penalize the output probabilities of the base LLM with an anti-expert LLM. Anti-expert methods are effective in mitigating hallucinations, but require high computational costs because the two LLMs are run simultaneously. In this paper, we propose an efficient anti-expert method called in-model anti-expert. It mitigated the hallucination problem with a single LLM and intervening to change the internal representations in the direction of improving factuality. Experiments results showed that the proposed method is less costly than the conventional anti-expert method and outperformed existing methods except for the anti-expert method. We confirmed that the proposed method improved GPU memory usage from 2.2x to 1.2x and latency from 1.9x to 1.2x.