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cs.CL2025
MICE for CATs: Model-Internal Confidence Estimation for Calibrating Agents with Tools
Nishant Subramani, Jason Eisner, Justin Svegliato +3
Tool-using agents that act in the world need to be both useful and safe. Well-calibrated model confidences can be used to weigh the risk versus reward of potential actions, but pri…
cs.CL2024
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts
Helia Hashemi, Jason Eisner, Corby Rosset +2
This paper introduces a framework for the automated evaluation of natural language texts. A manually constructed rubric describes how to assess multiple dimensions of interest. To…
cs.CL2024
Let's Think Var-by-Var: Large Language Models Enable Ad Hoc Probabilistic Reasoning
Shepard Xia, Brian Lu, Jason Eisner
A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in…