Summary of Calibrating Verbalized Probabilities For Large Language Models, by Cheng Wang et al.
Calibrating Verbalized Probabilities for Large Language Models
by Cheng Wang, Gyuri Szarvas, Georges Balazs, Pavel Danchenko, Patrick Ernst
First submitted to arxiv on: 9 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 Medium Difficulty Summary: This research paper presents a novel approach for calibrating verbalized probabilities from Large Language Models (LLMs). The authors investigate the capability of LLMs to generate probability distributions over categorical labels and identify an issue with re-softmax arising from scaling verbalized probabilities. They propose using the invert softmax trick to approximate the logit by inverting verbalized probabilities. The paper demonstrates the effectiveness of this approach on three public datasets, showcasing the robustness of LLMs in generating class distributions and facilitating post-calibration adjustments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research is about making large language models more reliable. These models are good at understanding what we say, but they don’t always give accurate answers. The authors found a way to fix this by using a trick called “invert softmax” that helps the model be more precise. They tested their idea on three different sets of data and showed that it works well. |
Keywords
» Artificial intelligence » Probability » Softmax
