Papers by Taro Yano
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents (2025.emnlp-main)
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| Challenge: | Existing approaches to optimize large language models rely on manual design or focus on optimizing individual components. |
| Approach: | They propose a LaMDAgent framework that constructs and optimizes end-to-end post-training pipelines by exploring various model improving methods, objects, and their applied orderings based on task-based feedback. |
| Outcome: | The proposed framework achieves a 9.0-point gain in tool-use accuracy without degrading instruction-following, and reduces computational costs. |
Can Large Language Models Invent Algorithms to Improve Themselves? (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance improvements, but the methods for improving LLMs are still designed by humans. |
| Approach: | They propose a framework which enables LLMs to generate and learn model-improvement algorithms by the seed model. |
| Outcome: | The proposed framework outperforms human-designed methods in model-improving tasks and improves the seed model by 6% and outperformed human-design methods by 4.3% on GSM8k. |
Relevance, Diversity, and Exclusivity: Designing Keyword-augmentation Strategy for Zero-shot Classifiers (2024.starsem-1)
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| Challenge: | Existing methods incorporate semantically similar keywords related to class names, but the properties of effective keywords remain unclear. |
| Approach: | They propose a method for acquiring keywords that satisfy these properties without additional knowledge bases or data. |
| Outcome: | The proposed method outperforms existing methods in fully zero-shot and generalized zero- shot settings. |