Summary of Functional-level Uncertainty Quantification For Calibrated Fine-tuning on Llms, by Ruijia Niu et al.
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
by Ruijia Niu, Dongxia Wu, Rose Yu, Yi-An Ma
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel approach to fine-tuning large language models (LLMs) for accurate uncertainty quantification is proposed in this paper. The authors focus on addressing the limitations of existing methods that struggle to generalize with limited data, leading to overconfidence in uncertain predictions. A new method called Functional-Level Uncertainty Quantification for Calibrated Fine-Tuning (UQ4CT) captures and calibrates epistemic uncertainty by hierarchically decomposing the functional space via a mixture-of-experts framework implemented during fine-tuning. This approach is evaluated on five benchmarks, demonstrating reduced Expected Calibration Error (ECE) by over 25% while maintaining high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at understanding us, but they’re not perfect. Sometimes they get things wrong, and it’s hard to tell when that happens. To make them more reliable, researchers have been working on ways to measure how sure the model is about its answers. This new method helps do just that by breaking down the process of generating text into smaller steps and keeping track of which parts are most uncertain. It works really well, even when the data it’s learning from changes a bit. |
Keywords
» Artificial intelligence » Fine tuning » Mixture of experts
