Papers by Ribeka Keyaki
Constructing a Japanese Verdict Prediction Dataset for Fact-Checking of LLM-Generated Texts (2026.acl-srw)
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Miwa Masano, Hirokazu Kiyomaru, Atsushi Keyaki, Kaito Horio, Rei Minamoto, Ribeka Keyaki, Kouta Nakayama, Hideyuki Tachibana, Daisuke Kawahara
| Challenge: | Text generated by Large Language Models (LLMs) may contain plausible but incorrect information known as hallucinations. |
| Approach: | They extend the label set for verdict prediction to capture claim-evidence relationships humans would commonly interpret as supported or refuted. |
| Outcome: | The proposed system improves F1 by 4 percentage points compared to baseline. |
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Existing fine-tuning techniques for information retrieval systems require learning query representations and query-document relations. |
| Approach: | They propose a method that bridges pre-training and fine-tuning by learning query representations and query-document relations in coarse-tuned models. |
| Outcome: | The proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. |