The Role of Ambiguity in Error Prediction via Uncertainty Quantification
The task of Error Prediction, namely predicting whether a model output is correct, is commonly tackled with Uncertainty Quantification (UQ). However, while uncertainty metrics capture when models lack knowledge or capacity to make a prediction, they also reflect aleatoric uncertainty, which is inherent in the model input and context. This paper presents a method for improving error prediction for Large Language Models (LLMs), by disentangling input ambiguity from UQ signal. We conduct experiments on the task of Question Answering (QA) with six UQ metrics and show that UQ metrics are more predictive of errors on unambiguous instances than on questions with multiple plausible answers. We us...
| Authors | Ieva Raminta Staliūnaitė, James Bishop, Andreas Vlachos |
|---|---|
| Primary category | cs.CL |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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