Papers by Hibiki Nakatani
A Text Embedding Model with Contrastive Example Mining for Point-of-Interest Geocoding (2025.coling-main)
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| Challenge: | Existing studies have focused on coarse-grained locations, but we focus on fine-grain POIs, which have many candidates with similar names. |
| Approach: | They develop a text embedding-based geocoding model and investigate (1) entry encoding representations and (2) hard negative mining approaches suitable for enhancing the model’s disambiguation ability. |
| Outcome: | The proposed model significantly improves its disambiguation ability and entry encoding representations. |
Reliability of Distribution Predictions by LLMs: Insights from Counterintuitive Pseudo-Distributions (2025.naacl-srw)
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Toma Suzuki, Ayuki Katayama, Seiji Gobara, Ryo Tsujimoto, Hibiki Nakatani, Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Recent studies highlight the use of Large Language Models (LLMs) for predicting response distributions as a cost-effective survey method. |
| Approach: | They examine whether LLMs can rationally estimate distributions when presented with explanations that are against commonsense. |
| Outcome: | The proposed models can rationally estimate distributions when presented with explanations that are against commonsense, but smaller or less human-optimized models follow explanations uncritically, compared to larger models that resist counterintuitive explanations by leveraging their pretraining-acquired knowledge. |