BAG prompts LLMs to reason over K sampled responses for strategy selection in multi-turn ambiguous QA, improving accuracy and faithfulness to uncertainty over baselines across six models.
Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Disentangling input ambiguity from uncertainty quantification improves error prediction for LLMs on QA tasks, yielding over 10 PRR point gains across models and datasets.
citing papers explorer
-
Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
BAG prompts LLMs to reason over K sampled responses for strategy selection in multi-turn ambiguous QA, improving accuracy and faithfulness to uncertainty over baselines across six models.
-
The Role of Ambiguity in Error Prediction via Uncertainty Quantification
Disentangling input ambiguity from uncertainty quantification improves error prediction for LLMs on QA tasks, yielding over 10 PRR point gains across models and datasets.