LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models

E Stengel-Eskin, P Hase, M Bansal - arxiv preprint arxiv:2405.21028, 2024 - arxiv.org
When answering questions, LLMs can convey not only an answer, but a level of confidence
about the answer being correct. This includes explicit confidence markers (eg giving a …

On Uncertainty In Natural Language Processing

D Ulmer - arxiv preprint arxiv:2410.03446, 2024 - arxiv.org
The last decade in deep learning has brought on increasingly capable systems that are
deployed on a wide variety of applications. In natural language processing, the field has …

-calibration of Language Model Confidence Scores for Generative QA

P Manggala, A Mastakouri, E Kirschbaum… - arxiv preprint arxiv …, 2024 - arxiv.org
To use generative question-and-answering (QA) systems for decision-making and in any
critical application, these systems need to provide well-calibrated confidence scores that …

Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for …

A Ahmed, M Saleem, M Alzeen, B Birur… - arxiv preprint arxiv …, 2025 - arxiv.org
Objective: To evaluate whether integrating large language models (LLMs) with traditional
machine learning approaches improves both the predictive accuracy and clinical …

LACIE: Listener-Aware Finetuning for Calibration in Large Language Models

E Stengel-Eskin, P Hase, M Bansal - The Thirty-eighth Annual Conference … - openreview.net
When answering questions, large language models (LLMs) can convey not only an answer
to the question, but a level of confidence about the answer being correct. This includes …