BAS aggregates utility from an answer-or-abstain model across risk thresholds and is uniquely maximized by truthful confidence estimates.
Gustafsson, Sean Wu, Anshul Thakur, and David A
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
PassiveQA trains models via supervised finetuning to decide Answer, Ask, or Abstain using structured information-state representations and knowledge-graph context, yielding better abstention and lower hallucination on QA datasets.
citing papers explorer
-
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
BAS aggregates utility from an answer-or-abstain model across risk thresholds and is uniquely maximized by truthful confidence estimates.
-
PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
PassiveQA trains models via supervised finetuning to decide Answer, Ask, or Abstain using structured information-state representations and knowledge-graph context, yielding better abstention and lower hallucination on QA datasets.