34 citations · 45 across the 13 of their papers we have counts for
3 papers · 1 filter
Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language
Diego Cerda-Mardini, Sarath Chandar, Sreenath Madathil
LLMs are increasingly deployed as post-hoc explainers of AI-generated outputs, yet it remains unclear whether they can reliably communicate probabilistic information in natural lan…
Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)
Nizar Islah, Istabrak Abbes, Irina Rish +2
When post-trained language models fail on reasoning problems, the common test-time-scaling response is to spend more compute on additional attempts, and the failed traces play no f…
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2
Language models are increasingly used in settings where outputs should follow an intended distribution, yet their generation probabilities are often poorly calibrated to it. In man…