Summary of Truthx: Alleviating Hallucinations by Editing Large Language Models in Truthful Space, By Shaolei Zhang et al.
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
by Shaolei Zhang, Tian Yu, Yang Feng
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have been observed to generate hallucinations, producing untruthful responses despite possessing correct knowledge. To unlock the full potential of LLMs, it is crucial to activate their truthfulness. This paper proposes TruthX, an inference-time intervention method that identifies and edits features within LLM’s internal representations governing truthfulness. TruthX employs an auto-encoder to map LLM’s representations into semantic and truthful latent spaces, using contrastive learning to identify a truthful editing direction. During inference, TruthX enhances the truthfulness of LLM by editing internal representations in truthful space. The proposed method improves the truthfulness of 13 advanced LLMs by an average of 20% on the TruthfulQA benchmark. Additionally, TruthX can control LLMs to produce truthful or hallucinatory responses by editing a single vector within their internal representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) sometimes make mistakes and give false answers even though they know the correct information. To fix this problem, researchers have developed a new method called TruthX. This method helps LLMs tell the truth more often by looking at how they represent information inside their own systems. During predictions, TruthX makes adjustments to help the model be more honest. In tests, TruthX improved the accuracy of 13 advanced LLMs by an average of 20%. It can even control the model to give truthful or false answers by making small changes. |
Keywords
* Artificial intelligence * Encoder * Inference
