Summary of Semantically Diverse Language Generation For Uncertainty Estimation in Language Models, by Lukas Aichberger et al.
Semantically Diverse Language Generation for Uncertainty Estimation in Language Models
by Lukas Aichberger, Kajetan Schweighofer, Mykyta Ielanskyi, Sepp Hochreiter
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 abstract presents a study that addresses the issue of hallucinations in large language models (LLMs). Hallucinations occur when an LLM generates text that is not supported by the input or training data. The authors suggest that these hallucinations are due to predictive uncertainty, where the model is unsure about the semantic meaning of the next tokens to generate. To tackle this issue, they introduce Semantically Diverse Language Generation (SDLG), a method that quantifies predictive uncertainty in LLMs and steers them to generate semantically diverse yet likely alternatives for an initially generated text. This approach provides a precise measure of aleatoric semantic uncertainty, detecting whether the initial text is likely to be hallucinated. The authors demonstrate the effectiveness of SDLG on question-answering tasks, showing that it outperforms existing methods while being computationally efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can sometimes make things up when generating text. This is called hallucination and it’s a problem because it makes these models untrustworthy. Right now, these models generate text by predicting what comes next. When they’re not sure about the meaning of the next words, they might start making things up. The authors of this study suggest that this happens because the model is uncertain about what to predict. They created a new method called SDLG (Semantically Diverse Language Generation) to measure how likely it is for an LLM to hallucinate. This approach helps detect when an LLM is making something up and provides a way to generate more accurate text. |
Keywords
» Artificial intelligence » Hallucination » Question answering
