10 papers
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…
Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation
Thomas Zollo, Jimmy Wang, Richard Zemel
Reasoning language models can solve increasingly complex tasks, but struggle to produce the calibrated confidence estimates necessary for reliable deployment. Existing calibration…
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…
Confidence Calibration in Vision-Language-Action Models
Thomas P Zollo, Richard Zemel
Trustworthy robot behavior requires not only high levels of task success but also that the robot can reliably quantify how likely it is to succeed. To this end, we present a first-…
Test-Time Warmup for Multimodal Large Language Models
Nikita Rajaneesh, Thomas Zollo, Richard Zemel
Multimodal Large Language Models (MLLMs) hold great promise for advanced reasoning at the intersection of text and images, yet they have not fully realized this potential. MLLMs ty…
Guiding LLM Decision-Making with Fairness Reward Models
Zara Hall, Melanie Subbiah, Thomas P Zollo +2
Large language models are increasingly used to support high-stakes decisions, potentially influencing who is granted bail or receives a loan. Naive chain-of-thought sampling can im…