Summary of Label-confidence-aware Uncertainty Estimation in Natural Language Generation, by Qinhong Lin et al.
Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation
by Qinhong Lin, Linna Zhou, Zhongliang Yang, Yuang Cai
First submitted to arxiv on: 10 Dec 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 paper explores the potential biases introduced by greedy decoding in Large Language Models (LLMs) when evaluating their output reliability. The authors argue that previous methods focusing on model entropy may overlook the uncertainties associated with the sources of labels, leading to biased classification outcomes. To address this issue, they introduce a label-confidence-aware (LCA) uncertainty estimation method based on Kullback-Leibler (KL) divergence, which bridges the gap between samples and label sources. This approach aims to enhance the reliability and stability of uncertainty assessments in LLMs. The authors empirically evaluate their proposed method across various popular LLMs and NLP datasets, demonstrating its effectiveness in capturing differences in sampling results and label sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that Large Language Models (LLMs) don’t give us wrong answers. Sometimes these models can make mistakes because they’re not very good at telling when they’re unsure or confused. The researchers want to fix this by creating a new way to measure how certain an LLM is about its answer. They call it the “label-confidence-aware” method, and it helps us understand that different ways of getting answers (like asking multiple people) can give us different results. This matters because we need to be able to trust what these models tell us. |
Keywords
» Artificial intelligence » Classification » Nlp
