Papers by Helinyi Peng
Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for uncertainty quantification in large language models rely on indirect signals, such as entropy across sampled generations, which can be difficult to interpret and do not fully leverage the model’s ability to assess its own uncertainty. |
| Approach: | They propose a method that groups sampled generations into semantically distinct clusters and uses the probability assigned by the LLM to each option as a confidence estimate. |
| Outcome: | The proposed method outperforms baseline methods and achieves competitive performance with as few as two additional samples. |