Summary of Sh2: Self-highlighted Hesitation Helps You Decode More Truthfully, by Jushi Kai et al.
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully
by Jushi Kai, Tianhang Zhang, Hai Hu, Zhouhan Lin
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Self-Highlighted Hesitation (SH2) method addresses the issue of hallucinations in large language models (LLMs). By leveraging information theory, SH2 identifies tokens with lower probabilities as more informative and closely related to factual information. This insight is used to “highlight” these tokens and force the model to hesitate during decoding, leading to improved factual knowledge extraction and reduced hallucinations. Experimental results demonstrate significant improvements on multiple tasks for LLaMA-7b, LLaMA2-7b, and Mistral-7b. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate text, but they sometimes make things up that aren’t true. A new method called Self-Highlighted Hesitation helps these models be more accurate by focusing on the most important words. This works because the model’s guesses about less common words are often related to facts, like names and descriptions. By highlighting these important words, the model is forced to think carefully before generating text, making it more likely to tell the truth. This method doesn’t need any extra data or models, and it helps large language models like LLaMA-7b and Mistral-7b be more accurate. |
Keywords
» Artificial intelligence » Llama
