Summary of Spuq: Perturbation-based Uncertainty Quantification For Large Language Models, by Xiang Gao et al.
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
by Xiang Gao, Jiaxin Zhang, Lalla Mouatadid, Kamalika Das
First submitted to arxiv on: 4 Mar 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 uncertainty quantification (UQ) method, sampling with perturbation for UQ (SPUQ), to address both aleatoric and epistemic uncertainties in large language models (LLMs). The method generates perturbations for LLM inputs, samples outputs for each perturbation, and incorporates an aggregation module. Experiments on various datasets demonstrate a substantial improvement in model uncertainty calibration, reducing Expected Calibration Error (ECE) by 50% on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate text, but they often make wrong predictions confidently. To fix this, scientists need to figure out how certain the models are about their answers. They came up with a new way to do this called SPUQ. It works by adding small changes to the words going into the model and then looking at many possible answers. This helps us understand how sure or unsure the model is. The researchers tested it on lots of text and found that it makes the models much more accurate. |
