4 citations · 4 across the 9 of their papers we have counts for
9 papers
Can AI Reason Like an Urban Planner? Benchmarking Large Language Models Against Professional Judgment
Yijie Deng, He Zhu, Wen Wang +3
Problem, Research Strategy, and Findings: The rise of large language models (LLMs) raises a key question for urban planning: which forms of professional planning knowledge can AI r…
ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQL
Hongzhou Zheng, Yixin Gou, Wenjia Zhang
Text-to-SQL translates natural language into executable SQL queries. Few-shot in-context learning methods built upon large language models (LLMs) achieve strong performance, yet th…
PlanBench-V: A Spatial Planning Map Benchmark for Vision-Language Models
Minxin Chen, He Zhu, Junyou Su +3
Spatial planning maps are central to territorial governance, translating planning objectives, regulations, and spatial strategies into visual forms for decision-making, public comm…
Anchored Supervised Fine-Tuning
He Zhu, Junyou Su, Peng Lai +4
Post-training of large language models involves a fundamental trade-off between supervised fine-tuning (SFT), which efficiently mimics demonstrations but tends to memorize, and rei…
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…
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…
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…
GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces
Boyang Li, Yulin Wu, Nuoxian Huang +1
Understanding spatial relationships across all dimensions is fundamental for intelligent systems. However, existing positional embeddings, such as Rotary Positional Embedding (RoPE…
PlanGPT: Enhancing Urban Planning with Tailored Language Model and Efficient Retrieval
He Zhu, Wenjia Zhang, Nuoxian Huang +9
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving…