Papers by Yatao Sun

2 papers
Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models (2025.acl-long)

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Challenge: Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency.
Approach: They propose a language-guided framework that integrates large language models with computer-automated design to address these challenges.
Outcome: The proposed framework outperforms traditional methods in accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts.
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

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Challenge: Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach: They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome: The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

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