Summary of Does Alignment Tuning Really Break Llms’ Internal Confidence?, by Hongseok Oh et al.
Does Alignment Tuning Really Break LLMs’ Internal Confidence?
by Hongseok Oh, Wonseok Hwang
First submitted to arxiv on: 31 Aug 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 study investigates the reliability of Large Language Models (LLMs) in real-world applications, focusing on their calibration degradation across four dimensions: models, calibration metrics, tasks, and confidence extraction methods. The analysis reveals that the relationship between alignment and calibration is not always a trade-off, but stricter conditions show that alignment consistently harms calibration. This highlights the need for careful measurement of model confidences and calibration errors, as well as future research into algorithms that can achieve both instruction-following and calibration without compromising either. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Large Language Models (LLMs) work in real-life situations. It shows that LLMs are not always accurate, even when they’re trained to be good at certain tasks. This is important because it means we need to find new ways to make sure LLMs are reliable and can do what they’re supposed to do. |
Keywords
» Artificial intelligence » Alignment
