activity
20192026
most citedHolistic Evaluation of Language Models

128 citations · 142 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CL2026

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…

cs.CL2025

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…

cs.CV2025

Can Visual Encoder Learn to See Arrows?

Naoyuki Terashita, Yusuke Tozaki, Hideaki Omote +4

The diagram is a visual representation of a relationship illustrated with edges (lines or arrows), which is widely used in industrial and scientific communication. Although recogni…

cs.CL2023★ 6 cited

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…

cs.CL2023★ 2 cited

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…

cs.CL2022★ 128 cited

Holistic Evaluation of Language Models

Percy Liang, Rishi Bommasani, Tony Lee +47

Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2020★ 1 cited

Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels using Statistical Sampling and Post-Processing

Manikandan Ravikiran, Amin Ekant Muljibhai, Toshinori Miyoshi +3

In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end,…

cs.CL2019★ 5 cited

Hitachi at MRP 2019: Unified Encoder-to-Biaffine Network for Cross-Framework Meaning Representation Parsing

Yuta Koreeda, Gaku Morio, Terufumi Morishita +2

This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating s…