Papers by Yusuke Yamauchi
Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have greatly improved natural language understanding and generation. |
| Approach: | They train a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks. |
| Outcome: | The results show that training–task synergies persist across all models while others vary substantially, emphasizing the importance of model-specific strategies. |
Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning (2026.acl-long)
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| Challenge: | Existing methods to induce circular emotion representations in language models are limited . elucidates trade-offs involved in applying circumplex models to deep learning architectures . |
| Approach: | They propose a method to induce circular emotion representations within language models via contrastive learning on a hypersphere. |
| Outcome: | The proposed method underperforms in high-dimensional settings and fine-grained classification. |
From Semantics to Style: A Cross-Dataset Comparative Framework for Sentence Similarity Predictions (2026.findings-eacl)
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| Challenge: | Existing frameworks for analyzing text embedding models are limited. |
| Approach: | They propose a framework that uses lightweight poolers to analyze STS, PI, and Triplet datasets. |
| Outcome: | The proposed framework shows that the model captures semantic differences between sentences and is consistent across datasets. |