Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering

Miles Shelton, Nate Wingerd, Kritim K Rijal, Ayush Garg, Adelina Gutic, Brett Barnes, Catherine Finegan-Dollak. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2025.

Authors

Ayush Garg (ORCID: https://orcid.org/0000-0001-6402-8529)
Catherine Finegan‐Dollak (ORCID: https://orcid.org/0000-0003-4316-565X)
B. Kim Barnes
Mercel J. Shelton
Nate Wingerd
Kritim K Rijal
Adelina Gutic

Institutions

University of Richmond (US)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.18653/v1/2025.acl-short.11
Primary Topic: Topic Modeling
Type: article
Field-Weighted Citation Impact: 0.00

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article

Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering

Ayush Garg, Catherine Finegan‐Dollak, B. Kim Barnes, Mercel J. Shelton et al.

Jan 1, 2025

Topic Modeling

article

Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering

Ayush Garg, Catherine Finegan‐Dollak, B. Kim Barnes, Mercel J. Shelton, Nate Wingerd, Kritim K Rijal, Adelina Gutic

January 1, 2025article en

Abstract

University of Richmond (US)

Openalex Percentile: Top 90%

Topic Modeling

DOI Open Access PDF OpenAlex

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Grounded, or a Good Guesser? A Per-Question Balanced Dataset to Separate Blind from Grounded Models for Embodied Question Answering — Ayush Garg, Catherine Finegan‐Dollak, et al. · (2025) | TGRS Research Map | TGRS