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…
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…
Decoding-Free Sampling Strategies for LLM Marginalization
David Pohl, Marco Cognetta, Junyoung Lee +1
Modern language models operate on subword-tokenized text in order to make a trade-off between model size, inference speed, and vocabulary coverage. A side effect of this is that, d…
Bit-level BPE: Below the byte boundary
Sangwhan Moon, Tatsuya Hiraoka, Naoaki Okazaki
Byte-level fallbacks for subword tokenization have become a common practice in large language models. In particular, it has been demonstrated to be incredibly effective as a pragma…
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…
Tokenization as Finite-State Transduction
Marco Cognetta, Naoaki Okazaki
Tokenization is the first step in modern neural language model pipelines where an input text is converted to a sequence of subword tokens. We introduce from first principles a fini…