7 papers
From Correspondence to Actions: Human-Like Multi-Image Spatial Reasoning in Multi-modal Large Language Models
Masanari Oi, Koki Maeda, Ryuto Koike +3
While multimodal large language models (MLLMs) have made substantial progress in single-image spatial reasoning, multi-image spatial reasoning, which requires integration of inform…
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…
JaWildText: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding
Koki Maeda, Naoaki Okazaki
Japanese scene text poses challenges that multilingual benchmarks often fail to capture, including mixed scripts, frequent vertical writing, and a character inventory far larger th…
Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
Koshiro Saito, Sakae Mizuki, Masanari Ohi +11
Why do we build local large language models (LLMs)? What should a local LLM learn from the target language? Which abilities can be transferred from other languages? Do language-spe…
Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models
Youmi Ma, Sakae Mizuki, Kazuki Fujii +12
Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized sol…