Summary of Clue: Concept-level Uncertainty Estimation For Large Language Models, by Yu-hsiang Wang et al.
CLUE: Concept-Level Uncertainty Estimation for Large Language Models
by Yu-Hsiang Wang, Andrew Bai, Che-Ping Tsai, Cho-Jui Hsieh
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Concept-Level Uncertainty Estimation (CLUE) framework improves the accuracy of Large Language Models’ (LLMs) natural language generation by separately assessing the uncertainty of each concept in a sequence. Building on existing LLMs, CLUE breaks down output sequences into individual concepts and measures their uncertainty, enabling more interpretable results. This innovation could enhance tasks like hallucination detection and story generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to measure how certain Large Language Models are about what they’re saying. Right now, these models are very good at generating text, but they don’t always explain why they chose certain words or phrases. The researchers developed a method called Concept-Level Uncertainty Estimation (CLUE) that helps figure out which parts of the generated text are most uncertain. This could be useful for tasks like detecting made-up information and creating original stories. |
Keywords
» Artificial intelligence » Hallucination
