5 citations · 5 across the 5 of their papers we have counts for
5 papers
Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization
Zhiqi Gao, Albert Ge, Alexander Berenbeim +2
Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter…
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Trilok Padhi, Ramneet Kaur, Krishiv Agarwal +9
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capabi…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
Ramneet Kaur, Colin Samplawski, Adam D. Cobb +8
In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Mode…
Measuring Classification Decision Certainty and Doubt
Alexander M. Berenbeim, Iain J. Cruickshank, Susmit Jha +2
Quantitative characterizations and estimations of uncertainty are of fundamental importance in optimization and decision-making processes. Herein, we propose intuitive scores, whic…