1 citations · 3 across the 12 of their papers we have counts for
3 papers · 1 filter
Binary Agreement for Efficient Unsupervised Confidence Calibration of Reasoning LLMs
Thomas Zollo, Jimmy Wang, Richard Zemel
Reasoning language models often lack the calibrated confidence needed for reliable deployment, particularly when correctness labels are unavailable and sampling budgets are limited…
Tell Me What To Learn: Generalizing Neural Memory to be Controllable in Natural Language
Max S. Bennett, Thomas P. Zollo, Richard Zemel
Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and…
Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions
Ruomeng Ding, Tianwei Gao, Thomas P. Zollo +3
Eliciting information to reduce uncertainty about latent group-level properties from surveys and other collective assessments requires allocating limited questioning effort under r…