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cs.CL2025
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
He Zhu, Zhiwen Ruan, Junyou Su +4
High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present TAG…
cs.CL2025
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models
He Zhu, Junyou Su, Minxin Chen +4
In the field of urban planning, existing Vision-Language Models (VLMs) frequently fail to effectively analyze and evaluate planning maps, despite the critical importance of these v…
cs.CL2024
FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only
He Zhu, Junyou Su, Tianle Lun +4
Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets ha…