2 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
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-…
Are Prompt-based Models Clueless?
Pride Kavumba, Ryo Takahashi, Yusuke Oda
Finetuning large pre-trained language models with a task-specific head has advanced the state-of-the-art on many natural language understanding benchmarks. However, models with a t…
Two Training Strategies for Improving Relation Extraction over Universal Graph
Qin Dai, Naoya Inoue, Ryo Takahashi +1
This paper explores how the Distantly Supervised Relation Extraction (DS-RE) can benefit from the use of a Universal Graph (UG), the combination of a Knowledge Graph (KG) and a lar…
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution
Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono +3
One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We ad…
Modeling Event Salience in Narratives via Barthes' Cardinal Functions
Takaki Otake, Sho Yokoi, Naoya Inoue +3
Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool fo…