Papers by Kazutoshi Shinoda
Let’s Put Ourselves in Sally’s Shoes: Shoes-of-Others Prefilling Improves Theory of Mind in Large Language Models (2026.findings-eacl)
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Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida, Yoshihiro Yamazaki, Keita Suzuki, Hiroaki Sugiyama, Kuniko Saito
| Challenge: | Existing methods for Theory of Mind (ToM) are specialized for inferring beliefs from contexts involving changes in the world state. |
| Approach: | They propose a method which makes fewer assumptions about contexts and is applicable to broader scenarios. |
| Outcome: | The proposed method makes fewer assumptions about contexts and is applicable to broader scenarios. |
Multi-style Generative Reading Comprehension (P19-1)
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Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| Challenge: | Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG). |
| Approach: | They propose a multi-style abstractive summarization model for question answering called Masque. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA. |
Debiasing Reward Models via Causally Motivated Inference-Time Intervention (2026.acl-long)
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| Challenge: | Existing approaches for mitigating spurious features in RMs focus on response length . Existing methods focus on RM activation, resulting in performance trade-offs . |
| Approach: | They propose a method that uses neurons to suppress spurious features in RMs at inference time. |
| Outcome: | The proposed method reduces sensitivity to spurious features without inducing performance trade-offs on RM benchmarks. |
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation (2021.acl-srw)
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| Challenge: | Existing data augmentation methods for reading comprehension lack robustness to challenge sets whose distribution is different from that of training sets. |
| Approach: | They propose a question-answer pair generation method that generates multiple diverse QA pairs from a paragraph to mitigate this problem. |
| Outcome: | The proposed model improves the accuracy of 12 challenge sets and the in-distribution accuracy. |