📝 Publications

Findings of ACL 2025 Self-Foveate: Enhancing Diversity and Difficulty of Synthesized Instructions from Unsupervised Text via Multi-Level Foveation, Mingzhe Li, Xin Lu, Yanyan Zhao.

• This work introduces an innovative LLM-driven method for instruction synthesis.
• Proposes a “Micro-Scatter-Macro” multi-level foveation methodology.
• Demonstrates superior performance across multiple unsupervised corpora and model architectures.