Papers by Shintaro Ozaki

5 papers
Diagnosing Vision Language Models’ Perception by Leveraging Human Methods for Color Vision Deficiencies (2026.eacl-long)

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Challenge: Large-scale Vision-Language Models (LVLMs) are being deployed in real-world settings that require visual inference.
Approach: They evaluate LVLMs' ability to account for variation in color perception using the Ishihara Test.
Outcome: The proposed models fail to reproduce the perceptual outcomes experienced by affected individuals and default to normative color perception.
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models (2025.findings-naacl)

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Challenge: LVLMs are increasingly capable of responding in multiple languages . however, there is a lack of evaluation tools for LVLs that handle multiple languages.
Approach: They used an extended dataset in multiple languages to evaluate LVLMs' ability to generate explanations in multiple language combinations.
Outcome: The proposed dataset in multiple languages evaluates LVLMs' ability to generate explanations in other languages.
BQA: Body Language Question Answering Dataset for Video Large Language Models (2025.acl-short)

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Challenge: a large part of human communication relies on nonverbal cues such as facial expressions, eye contact, and body language.
Approach: They propose to validate whether video large language models can correctly interpret body language from short clips of body language.
Outcome: The proposed model can correctly interpret emotions from short clips of body language.
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary? (2025.coling-main)

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Challenge: Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words . a recent study shows that frequency from YouTube subtitles is comparable to and often better than the best available resources.
Approach: They use YouTube subtitles to construct frequency norms for five languages . they find they are comparable to and often better than the best currently available resources .
Outcome: The proposed method improves on the best currently available resources for Chinese, English, Indonesian, Japanese, and Spanish.
LoCt-Instruct: An Automatic Pipeline for Constructing Datasets of Logical Continuous Instructions (2025.emnlp-main)

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Challenge: Existing instruction following datasets lack logical coherence across turns, narrow topical breadth and heavy manual effort.
Approach: They propose a pipeline that leverages LLMs’ reasoning capabilities to assemble rich, topic-related single-instruction data into multi-turn dialogues and produce chains that are logically coherent, progressively deepen in content, and span diverse domains without fixed templates or extensive human annotation.
Outcome: The proposed pipeline improves the performance of existing LLMs by integrating multiple topic-related data into multi-turn dialogues without fixed templates or extensive human annotation.

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