4 citations · 4 across the 13 of their papers we have counts for
6 papers · 1 filter
AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection
Peng Lai, He Zhu, Zhiwen Ruan +6
Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective alignment. Existing datasets a…
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…
Bridging the Agent-World Gap: Text World Models for LLM-based Agents
Yixia Li, Hongru Wang, Peng Lai +13
Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet m…
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…
Towards Fair and Comprehensive Evaluation of Routers in Collaborative LLM Systems
Wanxing Wu, He Zhu, Yixia Li +7
Large language models (LLMs) have achieved success, but cost and privacy constraints necessitate deploying smaller models locally while offloading complex queries to cloud-based mo…
InstructDiff: Domain-Adaptive Data Selection via Differential Entropy for Efficient LLM Fine-Tuning
Junyou Su, He Zhu, Xiao Luo +6
Supervised fine-tuning (SFT) is fundamental to adapting large language models, yet training on complete datasets incurs prohibitive costs with diminishing returns. Existing data se…