@inproceedings{sheta-etal-2025-behavioral,
title = "From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with {VLM}-Lens",
author = "Sheta, Hala and
Huang, Eric Haoran and
Wu, Shuyu and
Alenabi, Ilia and
Hong, Jiajun and
Lin, Ryker and
Ning, Ruoxi and
Wei, Daniel and
Yang, Jialin and
Zhou, Jiawei and
Ma, Ziqiao and
Shi, Freda",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.68/",
pages = "886--895",
ISBN = "979-8-89176-334-0",
abstract = "We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking, analysis, and interpretation of vision-language models (VLMs) by supporting the extraction of intermediate outputs from any layer during the forward pass of open-source VLMs. VLM-Lens provides a unified, YAML-configurable interface that abstracts away model-specific complexities and supports user-friendly operation across diverse VLMs. It currently supports 16 state-of-the-art base VLMs and their over 30 variants, and is extensible to accommodate new models without changing the core logic.The toolkit integrates easily with various interpretability and analysis methods. We demonstrate its usage with two simple analytical experiments, revealing systematic differences in the hidden representations of VLMs across layers and target concepts. VLM-Lens is released as an open-sourced project to accelerate community efforts in understanding and improving VLMs."
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%0 Conference Proceedings
%T From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens
%A Sheta, Hala
%A Huang, Eric Haoran
%A Wu, Shuyu
%A Alenabi, Ilia
%A Hong, Jiajun
%A Lin, Ryker
%A Ning, Ruoxi
%A Wei, Daniel
%A Yang, Jialin
%A Zhou, Jiawei
%A Ma, Ziqiao
%A Shi, Freda
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F sheta-etal-2025-behavioral
%X We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking, analysis, and interpretation of vision-language models (VLMs) by supporting the extraction of intermediate outputs from any layer during the forward pass of open-source VLMs. VLM-Lens provides a unified, YAML-configurable interface that abstracts away model-specific complexities and supports user-friendly operation across diverse VLMs. It currently supports 16 state-of-the-art base VLMs and their over 30 variants, and is extensible to accommodate new models without changing the core logic.The toolkit integrates easily with various interpretability and analysis methods. We demonstrate its usage with two simple analytical experiments, revealing systematic differences in the hidden representations of VLMs across layers and target concepts. VLM-Lens is released as an open-sourced project to accelerate community efforts in understanding and improving VLMs.
%U https://aclanthology.org/2025.emnlp-demos.68/
%P 886-895
Markdown (Informal)
[From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens](https://aclanthology.org/2025.emnlp-demos.68/) (Sheta et al., EMNLP 2025)
ACL
- Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, and Freda Shi. 2025. From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 886–895, Suzhou, China. Association for Computational Linguistics.