VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models

Haoyi Qiu, Wenbo Hu, Zi-Yi Dou, Nanyun Peng

Abstract
Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs, undermining their reliability. A comprehensive quantitative evaluation is necessary to identify and understand the extent of hallucinations in these models. However, existing benchmarks are often limited in scope, focusing mainly on object hallucinations. Furthermore, current evaluation methods struggle to effectively address the subtle semantic distinctions between model outputs and reference data, as well as the balance between hallucination and informativeness. To address these issues, we introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases. Moreover, we propose a large language model (LLM)-based two-stage evaluation framework that generalizes the popular CHAIR metric and incorporates both faithfulness and coverage into the evaluation. Experiments on 10 established LVLMs demonstrate that our evaluation metric is more comprehensive and better correlated with humans than existing work when evaluating on our challenging human-annotated benchmark dataset. Our work also highlights the critical balance between faithfulness and coverage of model outputs, and encourages future works to address hallucinations in LVLMs while keeping their outputs informative.
Anthology ID:
2024.findings-acl.105
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1783–1805
Language:
URL:
https://aclanthology.org/2024.findings-acl.105
DOI:
10.18653/v1/2024.findings-acl.105
Bibkey:
Cite (ACL):
Haoyi Qiu, Wenbo Hu, Zi-Yi Dou, and Nanyun Peng. 2024. VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1783–1805, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models (Qiu et al., Findings 2024)
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https://preview.aclanthology.org/add_acl24_videos/2024.findings-acl.105.pdf
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