6 citations · 14 across the 7 of their papers we have counts for
8 papers · 1 filter
Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks
Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita +3
When applying LLMs to real-world enterprise operations, LLMs need to handle proprietary knowledge in small domains of specific operations (). A previous stu…
Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains
Yawen Xue, Masaya Tsunokake, Yuta Koreeda +3
Agentic large language models (LLMs) have become prominent for autonomously interacting with external environments and performing multi-step reasoning tasks. Most approaches levera…
LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
Yuta Koreeda, Terufumi Morishita, Osamu Imaichi +1
Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatic…
Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
Yuta Koreeda, Ken-ichi Yokote, Hiroaki Ozaki +3
This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setu…
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
Yuta Koreeda, Christopher D. Manning
Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose "document-le…
Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser
Yuta Koreeda, Christopher D. Manning
While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visua…