Papers by Kazuki Yano
STEP: Staged Parameter-Efficient Pre-training for Large Language Models (2025.naacl-short)
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| Challenge: | Recent LLM development trends involve pre-training models with a vast number of parameters on massive datasets. |
| Approach: | They propose a method that integrates parameter-efficient tuning techniques with model growth to reduce memory requirements while maintaining equivalent performance. |
| Outcome: | The proposed method reduces memory requirements by 53.9% while maintaining equivalent performance to vanilla pre-trained models on downstream tasks. |
STEP: Staged Parameter-Efficient Pre-training for Large Language Models (2024.acl-srw)
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| Challenge: | Existing methods for reducing computational costs during pre-training have been studied, but they often degrade performance under fair conditions. |
| Approach: | They propose a method that combines parameter-efficient tuning and staged training to reduce memory requirements while maintaining comparable performance. |
| Outcome: | The proposed method reduces memory requirements by 40.4% while maintaining comparable performance. |
TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks (2026.eacl-long)
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| Challenge: | Automated software engineering is a critical task of software engineers. |
| Approach: | They propose a benchmark to evaluate software migration in real-world Python projects. |
| Outcome: | The proposed benchmark consists of GitHub repositories whose tests fail in response to dependency updates. |
Suppressing Final Layer Hidden State Jumps in Transformer Pretraining (2026.findings-eacl)
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| Challenge: | Existing models exhibit only slight changes in the angular distance between the input and output hidden state vectors in the middle layers . |
| Approach: | They propose a jump-suppressing regularizer which penalizes large hidden state displacements near the final layer during pre-training. |
| Outcome: | The proposed method significantly reduces hidden state jumps in the final layer and increases model capacity. |