Summary of Semantic Entropy Probes: Robust and Cheap Hallucination Detection in Llms, by Jannik Kossen et al.
Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs
by Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, Yarin Gal
First submitted to arxiv on: 22 Jun 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 The proposed semantic entropy probes (SEPs) offer a cost-effective solution for uncertainty quantification in Large Language Models (LLMs). Hallucinations, which generate plausible but incorrect text, hinder practical adoption of LLMs. Building on recent work by Farquhar et al. (2024), SEPs directly approximate semantic entropy from single model generations, reducing computation costs to almost zero. This method retains high performance for hallucination detection and generalizes better than previous probing methods. The results suggest that model hidden states capture semantic entropy, with insights into token positions and model layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to make language models more reliable! Right now, these powerful tools can sometimes generate fake text that sounds real but isn’t true. To fix this, we created a new way to measure how certain the model is about what it’s saying. This new method is fast and doesn’t need lots of extra calculations, so it’s perfect for using in real-life applications. We tested it and found that it works well even when faced with unknown or unexpected situations. It also helps us understand which parts of the language model are most important for making accurate predictions. |
Keywords
» Artificial intelligence » Hallucination » Language model » Token
