Summary of Graph-based Confidence Calibration For Large Language Models, by Yukun Li et al.
Graph-based Confidence Calibration for Large Language Models
by Yukun Li, Sijia Wang, Lifu Huang, Li-Ping Liu
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 researchers propose a novel method for improving the reliability of large language models by providing accurate confidence estimations regarding the correctness of their answers. The method combines the model’s self-consistency with labeled data and trains an auxiliary model to estimate the correctness of its responses to questions. This is achieved by representing the consistency among the model’s multiple responses to a question using a weighted graph, assigning correctness labels based on their similarity to the correct answer, and training a graph neural network to estimate the probability of correct responses. The proposed approach substantially outperforms several recent methods in confidence calibration across multiple benchmark datasets and improves generalization capability on out-of-domain data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that language models are more reliable by giving them accurate ways to say how confident they are in their answers. It’s tricky because the models can make mistakes, but it’s hard to detect those mistakes. The researchers came up with a new way to do this using labeled data and training an extra model to figure out if the language model is correct or not. They used special graphs and networks to get this done. Their method works really well on big datasets and even does better when dealing with new, unseen data. |
Keywords
» Artificial intelligence » Generalization » Graph neural network » Language model » Probability
