Summary of Uncertainty Estimation and Quantification For Llms: a Simple Supervised Approach, by Linyu Liu et al.
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
by Linyu Liu, Yu Pan, Xiaocheng Li, Guanting Chen
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 This paper focuses on improving uncertainty estimation and calibration for Large Language Models (LLMs). The authors formulate the uncertainty estimation problem, a relatively unexplored area in existing literature. They propose a supervised approach that uses labeled datasets to estimate uncertainty in LLMs’ responses. This method leverages hidden neurons in LLMs, which may contain uncertainty information. The authors demonstrate the benefits of this approach across various tasks and show robust transferability in out-of-distribution settings. Additionally, they distinguish between uncertainty estimation and calibration, showing that better uncertainty estimation leads to better calibration performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models (LLMs) more accurate and reliable. The authors want to figure out how well LLMs are really doing when they give answers. They propose a new way of doing this by using labeled data to estimate the uncertainty in LLMs’ responses. This method looks at the hidden parts of the LLMs, which might contain clues about their uncertainty. The results show that this approach works well across different tasks and is useful even when the model is outside its usual range. |
Keywords
» Artificial intelligence » Supervised » Transferability
