Papers by Yuta Koreeda
Disentangling the Effects of Unlearning in Measuring Parametric Faithfulness of Chain-of-Thought (2026.acl-srw)
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Ryo Mitsuhashi, Gaku Morio, Ayana Niwa, Masahiro Kaneko, Kentaro Inui, Terufumi Morishita, Yuta Koreeda, Yasuhiro Sogawa
| Challenge: | Chain-of-Thought (CoT) has been debated as a model's faithfulness to internal reasoning process. |
| Approach: | They propose to use unlearning to measure parametric faithfulness of models by adjusting for unintended artifacts of unlearning. |
| Outcome: | The proposed metric accounts for the unintended artifacts of unlearning and shows that it is non-negligible. |
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization (2020.acl-main)
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| Challenge: | Argument mining studies have advanced the ability to predict argument structures, but the technology for capturing non-tree-structured arguments is still in its infancy. |
| Approach: | They propose a neural model that can predict proposition types and edges between propositions. |
| Outcome: | The proposed model improves edge prediction performance compared to baseline models. |
Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks (2026.eacl-industry)
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Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita, Koichi Nagatsuka, Hikaru Tomonari, Yasuhiro Sogawa
| Challenge: | Domain-adaptive pre-training (DAPT) is one approach for enabling LLMs to handle unseen knowledge. |
| Approach: | They propose to disentangle the answering process into three subtasks and evaluate the performance of each subtask. |
| Outcome: | The proposed model resolves the elicitation task that the base model struggled with but does not resolve other subtasks. |
Acquiring Bidirectionality via Large and Small Language Models (2025.coling-main)
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| Challenge: | Existing unidirectional language models are still used for token-level classification tasks, but they lack bidirectionality. |
| Approach: | They propose to use bidirectional language models to train a small backward LM and concatenate its representations to those of an existing LM for downstream tasks. |
| Outcome: | The proposed model improves performance by more than 10 points in token-classification tasks and in rare domains. |
ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts (2021.findings-emnlp)
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| Challenge: | Contract review is a time-consuming procedure that costs companies millions of dollars each year . linguistic characteristics of contracts, such as negations by exceptions, contribute to the difficulty of this task . |
| Approach: | They propose a document-level natural language inference (NLI) task for contracts . they annotate and release the largest corpus to date consisting of 607 annotated contracts a linguistically rich system is proposed . |
| Outcome: | The proposed system is based on a contract review task that includes 607 annotated contracts. |
SParK-Eval: Evaluating Structure-Aware Knowledge Acquisition in LLMs for Domain Adaptation to Industrial Records (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) often struggle in domain adaptation for industrial settings where available corpora are limited and structurally diverse. |
| Approach: | They propose a framework that constructs question–answer pairs from pretraining data and annotates each with its input structure. |
| Outcome: | The proposed framework can be used to analyze how input structure affects parametric knowledge acquisition during domain-adaptive pretraining. |