HyperEdit: Mitigating Hallucinations of Large Language Models via Hyperbolic Representation Editing

Abstract

Large language models (LLMs) often generate hallucinations, undermining the reliability of their outputs. While prior work improves truthfulness by contrastive decoding or representation editing, these approaches overlook the hierarchical relationship between truthful and untruthful content, thereby constraining the LLM's knowledge potential. In this paper, we propose HyperEdit, a novel inference-time intervention method that encodes truthful and untruthful content as an entailment hierarchy and performs representation editing in the hyperbolic space to activate the truthfulness of LLMs. Specifically, we employ an auto-encoder to project the representations of LLMs into a hyperbolic space where truthful samples lie closer to the origin and untruthful ones farther away. This results in a truthfulness-aware hyperbolic space, where proximity to the origin indicates higher truthfulness, defining a natural editing direction. During inference, we edit the LLM's internal representations along this hyperbolic direction to elicit more truthful outputs. Experimental results on TruthfulQA and three additional benchmarks demonstrate that HyperEdit consistently enhances truthfulness across various advanced LLMs.