10 papers
Neuron Level Analysis of Large Language Model in Legal Domain Reasoning
Eri Onami, Youmi Ma, Shuhei Kurita +1
We presented a neuron-level analysis of legal-domain reasoning in LLMs, comparing it with other applied domain tasks across seven open-weight models. Using neuron attribution score…
HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers
Issa Sugiura, Shuhei Kurita, Yusuke Oda +1
Understanding chart and table images is essential for applying vision-language models (VLMs) to real-world document understanding. While English benchmarks have advanced rapidly, n…
WAON: A Large-Scale Japanese Image-Text Dataset for Cultural Adaptation in Contrastive Vision-Language Models
Issa Sugiura, Shuhei Kurita, Yusuke Oda +3
Contrastive vision-language models have achieved remarkable progress through large-scale pretraining. Recent work has shown that removing English-only caption filters and pretraini…
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Daichi Yashima, Shuhei Kurita, Yusuke Oda +3
In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging because of their intricate…
JAMMEval: A Refined Collection of Japanese Benchmarks for Reliable VLM Evaluation
Issa Sugiura, Koki Maeda, Shuhei Kurita +3
Reliable evaluation is essential for the development of vision-language models (VLMs). However, Japanese VQA benchmarks have undergone far less iterative refinement than their Engl…
Jagle: Building a Large-Scale Japanese Multimodal Post-Training Dataset for Vision-Language Models
Issa Sugiura, Keito Sasagawa, Keisuke Nakao +8
Developing vision-language models (VLMs) that generalize across diverse tasks requires large-scale training datasets with diverse content. In English, such datasets are typically c…