5 citations · 5 across the 10 of their papers we have counts for
11 papers
Synth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images
Keito Sasagawa, Shuhei Kurita, Daisuke Kawahara
The ability of Large Vision Language Models (LVLMs) to read text within document images is crucial, as it enables various applications such as Document Visual Question Answering. T…
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…
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…
HiFlow: Tokenization-Free Scale-Wise Autoregressive Policy Learning via Flow Matching
Daichi Yashima, Koki Seno, Shuhei Kurita +2
Coarse-to-fine autoregressive modeling has recently shown strong promise for visuomotor policy learning, combining the inference efficiency of autoregressive methods with the globa…
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…
Llama-Mimi: Exploring the Limits of Flattened Speech Language Modeling
Issa Sugiura, Shuhei Kurita, Yusuke Oda +1
Speech Language Models (SpeechLMs) model tokenized speech to capture both semantic and acoustic information. When neural audio codecs based on Residual Vector Quantization (RVQ) ar…
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model
Keito Sasagawa, Koki Maeda, Issa Sugiura +3
To develop high-performing Visual Language Models (VLMs), it is essential to prepare multimodal resources, such as image-text pairs, interleaved data, and instruction data. While m…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…