@inproceedings{padhi-etal-2026-cross,
title = "Cross-Modal Grounding for Calibrated Confidence in Vision Language Models",
author = "Padhi, Trilok and
Kaur, Ramneet and
Cobb, Adam D. and
Acharya, Manoj and
Samplawski, Colin and
Matejek, Brian and
Berenbeim, Alexander Michael and
Bastian, Nathaniel D. and
Jha, Susmit and
Kursuncu, Ugur and
Roy, Anirban",
editor = "Wang, Yimu and
Choi, Yee Man and
Wu, Di and
Ouyang, Siqi and
Azadani, Mozhgan Nasr and
Fung, Yi R. (May)",
booktitle = "Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently ({G}round{LM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.groundlm-1.39/",
pages = "480--504",
ISBN = "979-8-89176-478-1",
abstract = "We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for visual language models (VLMs). Existing state-of-the-art UQ methods rely on consistency among multiple responses, but often report higher confidence when the model is consistently incorrect, leading to poorly calibrated confidence. To address this, we leverage cross-modal consistency by grounding back the textual responses to visual inputs. The confidence from the grounding model (GM) is used to calibrate the overall confidence. Given that using a GM adds its own uncertainty, we apply temperature scaling to calibrate the GM{'}s confidence in the accuracy of the generated responses. We comprehensively evaluate the proposed approach across diverse range of settings, including datasets with varying visuo-linguistic complexity, Q{\&}A and caption generation tasks, general visual and specialized medical contexts, VLMs differing in scale, openness, and different categories of GMs. Experimental results show that our approach consistently improves confidence calibration, with expected calibration error (ECE) improvements ranging from 32.5{\%} to 96.7{\%} over both self and non-self consistency-based baselines. Code is available here https://github.com/trilokpadhi/Multimodal-Uncertainty-Quantification."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="padhi-etal-2026-cross">
<titleInfo>
<title>Cross-Modal Grounding for Calibrated Confidence in Vision Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Trilok</namePart>
<namePart type="family">Padhi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ramneet</namePart>
<namePart type="family">Kaur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adam</namePart>
<namePart type="given">D</namePart>
<namePart type="family">Cobb</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manoj</namePart>
<namePart type="family">Acharya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Colin</namePart>
<namePart type="family">Samplawski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brian</namePart>
<namePart type="family">Matejek</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="given">Michael</namePart>
<namePart type="family">Berenbeim</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathaniel</namePart>
<namePart type="given">D</namePart>
<namePart type="family">Bastian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Susmit</namePart>
<namePart type="family">Jha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ugur</namePart>
<namePart type="family">Kursuncu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anirban</namePart>
<namePart type="family">Roy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yimu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yee</namePart>
<namePart type="given">Man</namePart>
<namePart type="family">Choi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Di</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Siqi</namePart>
<namePart type="family">Ouyang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mozhgan</namePart>
<namePart type="given">Nasr</namePart>
<namePart type="family">Azadani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="given">R</namePart>
<namePart type="given">(May)</namePart>
<namePart type="family">Fung</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-478-1</identifier>
</relatedItem>
<abstract>We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for visual language models (VLMs). Existing state-of-the-art UQ methods rely on consistency among multiple responses, but often report higher confidence when the model is consistently incorrect, leading to poorly calibrated confidence. To address this, we leverage cross-modal consistency by grounding back the textual responses to visual inputs. The confidence from the grounding model (GM) is used to calibrate the overall confidence. Given that using a GM adds its own uncertainty, we apply temperature scaling to calibrate the GM’s confidence in the accuracy of the generated responses. We comprehensively evaluate the proposed approach across diverse range of settings, including datasets with varying visuo-linguistic complexity, Q&A and caption generation tasks, general visual and specialized medical contexts, VLMs differing in scale, openness, and different categories of GMs. Experimental results show that our approach consistently improves confidence calibration, with expected calibration error (ECE) improvements ranging from 32.5% to 96.7% over both self and non-self consistency-based baselines. Code is available here https://github.com/trilokpadhi/Multimodal-Uncertainty-Quantification.</abstract>
<identifier type="citekey">padhi-etal-2026-cross</identifier>
<location>
<url>https://aclanthology.org/2026.groundlm-1.39/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>480</start>
<end>504</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Cross-Modal Grounding for Calibrated Confidence in Vision Language Models
%A Padhi, Trilok
%A Kaur, Ramneet
%A Cobb, Adam D.
%A Acharya, Manoj
%A Samplawski, Colin
%A Matejek, Brian
%A Berenbeim, Alexander Michael
%A Bastian, Nathaniel D.
%A Jha, Susmit
%A Kursuncu, Ugur
%A Roy, Anirban
%Y Wang, Yimu
%Y Choi, Yee Man
%Y Wu, Di
%Y Ouyang, Siqi
%Y Azadani, Mozhgan Nasr
%Y Fung, Yi R. (May)
%S Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-478-1
%F padhi-etal-2026-cross
%X We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for visual language models (VLMs). Existing state-of-the-art UQ methods rely on consistency among multiple responses, but often report higher confidence when the model is consistently incorrect, leading to poorly calibrated confidence. To address this, we leverage cross-modal consistency by grounding back the textual responses to visual inputs. The confidence from the grounding model (GM) is used to calibrate the overall confidence. Given that using a GM adds its own uncertainty, we apply temperature scaling to calibrate the GM’s confidence in the accuracy of the generated responses. We comprehensively evaluate the proposed approach across diverse range of settings, including datasets with varying visuo-linguistic complexity, Q&A and caption generation tasks, general visual and specialized medical contexts, VLMs differing in scale, openness, and different categories of GMs. Experimental results show that our approach consistently improves confidence calibration, with expected calibration error (ECE) improvements ranging from 32.5% to 96.7% over both self and non-self consistency-based baselines. Code is available here https://github.com/trilokpadhi/Multimodal-Uncertainty-Quantification.
%U https://aclanthology.org/2026.groundlm-1.39/
%P 480-504
Markdown (Informal)
[Cross-Modal Grounding for Calibrated Confidence in Vision Language Models](https://aclanthology.org/2026.groundlm-1.39/) (Padhi et al., GroundLM 2026)
ACL
- Trilok Padhi, Ramneet Kaur, Adam D. Cobb, Manoj Acharya, Colin Samplawski, Brian Matejek, Alexander Michael Berenbeim, Nathaniel D. Bastian, Susmit Jha, Ugur Kursuncu, and Anirban Roy. 2026. Cross-Modal Grounding for Calibrated Confidence in Vision Language Models. In Proceedings of the 1st Workshop on Grounding Language Models: Learning Faithfully and Efficiently (GroundLM 2026), pages 480–504, Budapest, Hungary. Association for Computational Linguistics.