Summary of Atomic Calibration Of Llms in Long-form Generations, by Caiqi Zhang et al.
Atomic Calibration of LLMs in Long-Form Generations
by Caiqi Zhang, Ruihan Yang, Zhisong Zhang, Xinting Huang, Sen Yang, Dong Yu, Nigel Collier
First submitted to arxiv on: 17 Oct 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 paper introduces a novel approach to confidence calibration for large language models (LLMs) called atomic calibration. Traditional methods focus on short-form tasks and provide a single confidence score at the response level, which is insufficient for long-form generations. Atomic calibration evaluates factuality calibration at a fine-grained level by breaking down responses into atomic claims. The authors demonstrate that combining discriminative and generative types of confidence elicitation methods can enhance calibration. Extensive experiments on various LLMs and datasets show that atomic calibration is well-suited for long-form generation and can also improve macro calibration results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make large language models more trustworthy by figuring out how confident they are in what they’re saying. Right now, these models often get things wrong or say things that aren’t true. To fix this, the researchers came up with a new method called “atomic calibration”. This method looks at how confident the model is about specific parts of its answer, rather than just looking at the whole answer like other methods do. They tested their idea on lots of different models and datasets and found that it works really well for longer answers and can even make shorter answers better too. |
