2 citations · 2 across the 6 of their papers we have counts for
7 papers
(How) Do MLLMs Report Bistable Images Like Humans?
Ryota Takatsuki, Tomoki Doi, Amane Watahiki +2
Bistable images such as the duck-rabbit are classic stimuli in which one image supports multiple mutually incompatible interpretations, typically reported one at a time in humans.…
Evaluation of Multilingual Ability to Use Spatial Deictic Expressions in Vision-Language Models
Kaito Watanabe, Taisei Yamamoto, Tomoki Doi +1
One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial reasoning abilities of VLMs, we…
Developing a Guideline for the Labovian-Structural Analysis of Oral Narratives in Japanese
Amane Watahiki, Tomoki Doi, Akari Kikuchi +5
Narrative analysis is a cornerstone of qualitative research. One leading approach is the Labovian model, but its application is labor-intensive, requiring a holistic, recursive int…
Investigating Training and Generalization in Faithful Self-Explanations of Large Language Models
Tomoki Doi, Masaru Isonuma, Hitomi Yanaka
Large language models have the potential to generate explanations for their own predictions in a variety of styles based on user instructions. Recent research has examined whether…
Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports
Amane Watahiki, Tomoki Doi, Taiga Shinozaki +4
One of the main objectives in developing large vision-language models (LVLMs) is to engineer systems that can assist humans with multimodal tasks, including interpreting descriptio…
Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions?
Taiga Shinozaki, Tomoki Doi, Amane Watahiki +2
Humans are susceptible to optical illusions, which serve as valuable tools for investigating sensory and cognitive processes. Inspired by human vision studies, research has begun e…
Comprehensive Evaluation of Large Language Models for Topic Modeling
Tomoki Doi, Masaru Isonuma, Hitomi Yanaka
Recent work utilizes Large Language Models (LLMs) for topic modeling, generating comprehensible topic labels for given documents. However, their performance has mainly been evaluat…