289 citations · 309 across the 7 of their papers we have counts for
9 papers
LLM-jp-4 Technical Report
LLM-jp, :, Chaoran Liu +16
This paper presents LLM-jp-4, a family of Japanese sovereign large language models (LLMs) developed with an emphasis on transparency, reproducibility, and practical usability. LLM-…
BIS Reasoning 1.0: The First Large-Scale Japanese Benchmark for Belief-Inconsistent Syllogistic Reasoning
Ha-Thanh Nguyen, Hideyuki Tachibana, Chaoran Liu +4
We present BIS Reasoning 1.0, the first large-scale Japanese dataset of syllogistic reasoning problems explicitly designed to evaluate belief-inconsistent reasoning in large langua…
RAD: Redundancy-Aware Distillation for Hybrid Models via Self-Speculative Decoding
Yuichiro Hoshino, Hideyuki Tachibana, Muneyoshi Inahara +1
Hybrid models combining Transformers and State Space Models (SSMs) are promising for balancing performance and efficiency. However, optimizing these hybrid models, particularly by…
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications
Chihiro Yano, Akihiko Fukuchi, Shoko Fukasawa +2
Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conve…
gSwin: Gated MLP Vision Model with Hierarchical Structure of Shifted Window
Mocho Go, Hideyuki Tachibana
Following the success in language domain, the self-attention mechanism (transformer) is adopted in the vision domain and achieving great success recently. Additionally, as another…
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives
Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara +2
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of n…
Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn's Algorithm
Hideyuki Tachibana
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the or…
Accent Estimation of Japanese Words from Their Surfaces and Romanizations for Building Large Vocabulary Accent Dictionaries
Hideyuki Tachibana, Yotaro Katayama
In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence. However, there are a limited number of publicly available accent dictionaries, an…
Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
Hideyuki Tachibana, Katsuya Uenoyama, Shunsuke Aihara
This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units. Recurrent neural networks (RNN) h…