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- + Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, they sometimes suffer from producing hallucinations, particularly in cases where they may generate untruthful responses despite possessing the correct knowledge. In this paper, we propose TruthX, an inferencetime method to elicit the truthfulness of LLMs by editing their internal representations in truthful space. TruthX employs an auto-encoder to map LLM’s representations into semantic and truthful latent spaces respectively, and applies contrastive learning to identify a truthful editing direction within the truthful space. During inference, by editing LLM’s internal representations in truthful space, TruthX effectively enhances the truthfulness of LLMs. Experiments show that TruthX effectively improves the truthfulness of 13 advanced LLMs by an average of 20% on TruthfulQA benchmark. Further analyses suggest that the truthful space acquired by TruthX plays a pivotal role in controlling LLM to produce truthful or hallucinatory responses. +
BibTex Code Here+ If you have any questions, please contact Shaolei Zhang (zhangshaolei20z@ict.ac.cn). +
+@inproceedings{truthx,
+ title={TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space},
+ author={Shaolei Zhang and Tian Yu and Yang Feng},
+ year={2024},
+ url={https://arxiv.org/abs/2402.17811}
+ booktitle = {Proceedings of the 62th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+ year = {2024},
+ publisher = {Association for Computational Linguistics},
+}
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+ TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space (ACL 2024)