Summary of Calibrate to Discriminate: Improve In-context Learning with Label-free Comparative Inference, by Wei Cheng et al.
Calibrate to Discriminate: Improve In-Context Learning with Label-Free Comparative Inference
by Wei Cheng, Tianlu Wang, Yanmin Ji, Fan Yang, Keren Tan, Yiyu Zheng
First submitted to arxiv on: 3 Oct 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 A new study reveals that large language models (LLMs) exhibit a previously unknown issue called indiscriminate miscalibration, where both correct and incorrect predictions are assigned the same level of confidence. The authors propose novel metrics to measure this behavior and develop an in-context comparative inference method to alleviate miscalibrations and improve classification performance. Experimental results on five datasets show that their approach can achieve more accurate and calibrated predictions compared to regular zero-shot and few-shot prompting methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are really smart computers that can understand and respond to human language. Recently, researchers have been experimenting with how well these models can learn new things without needing to be trained on huge amounts of data first. However, they’ve found a problem where the model is not very good at knowing when it’s right or wrong. This means it might say something is true even if it’s actually false, and vice versa! The scientists propose some new ways to measure this issue and come up with a solution that makes their predictions more reliable. |
Keywords
» Artificial intelligence » Classification » Few shot » Inference » Prompting » Zero shot
