Papers by Ryo Kamoi
WiCE: Real-World Entailment for Claims in Wikipedia (2023.emnlp-main)
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| Challenge: | Textual entailment models are increasingly used in fact-checking, presupposition verification in question answering, or summary evaluation. |
| Approach: | They propose a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia that provides en-tailment judgments over sub-sentence units of the claim and a minimal subset of evidence sentences that support each subclaim. |
| Outcome: | The proposed dataset improves on multiple datasets at test time and shows that real claims involve verification and retrieval problems that existing models fail to address. |
Shortcomings of Question Answering Based Factuality Frameworks for Error Localization (2023.eacl-main)
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| Challenge: | Abstractive summarization systems often generate summaries with factual errors . many approaches to detect these errors have been proposed, but this capability has not been evaluated in past research . |
| Approach: | They propose to use question answering-based factuality metrics to detect errors in summaries . they find that QA-based frameworks fail to correctly identify error spans in generated summary . |
| Outcome: | The proposed methods outperform trivial exact match baselines in localizing errors in summaries. |
Fair Abstractive Summarization of Diverse Perspectives (2024.naacl-long)
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Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang
| Challenge: | Existing work on summarization metrics and large language models has not explored fair abstractive summarizing. |
| Approach: | They propose four reference-free automatic metrics to measure the differences between target and source perspectives. |
| Outcome: | The proposed methods alleviate fair abstractive summarization on user-generated data. |
Efficient PRM Training Data Synthesis via Formal Verification (2026.findings-acl)
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Ryo Kamoi, Yusen Zhang, Nan Zhang, Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, Wenpeng Yin, Rui Zhang
| Challenge: | Existing approaches for constructing PRM training data rely on human annotation or sampling-based labeling methods that require repeated LLM calls. |
| Approach: | They propose a framework that synthesizes PRM training data by annotating step-level error labels using formal verification tools such as Z3 and Isabelle. |
| Outcome: | The proposed framework synthesizes PRM training data from formal logic and theorem proving tasks without human annotation or additional LLM calls. |