2 citations · 2 across the 5 of their papers we have counts for
6 papers
CMDR: Contextual Multimodal Document Retrieval
Ryota Tanaka, Taku Hasegawa, Kyosuke Nishida
Multimodal document retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, existing benchmarks primarily ev…
Parallel In-context Learning for Large Vision Language Models
Shin'ya Yamaguchi, Daiki Chijiwa, Tamao Sakao +1
Large vision-language models (LVLMs) employ multi-modal in-context learning (MM-ICL) to adapt to new tasks by leveraging demonstration examples. While increasing the number of demo…
Lossless Vocabulary Reduction for Auto-Regressive Language Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida +4
Tokenization -- the process of decomposing a given text into a sequence of subwords called tokens -- is one of the key components in the development of language models. Particularl…
VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents
Ryota Tanaka, Taichi Iki, Taku Hasegawa +3
We aim to develop a retrieval-augmented generation (RAG) framework that answers questions over a corpus of visually-rich documents presented in mixed modalities (e.g., charts, tabl…
Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models
Daiki Chijiwa, Taku Hasegawa, Kyosuke Nishida +2
While foundation models have been exploited for various expert tasks through fine-tuning, any foundation model will become outdated due to its old knowledge or limited capability.…
Wavelet-based Positional Representation for Long Context
Yui Oka, Taku Hasegawa, Kyosuke Nishida +1
In the realm of large-scale language models, a significant challenge arises when extrapolating sequences beyond the maximum allowable length. This is because the model's position e…