4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Fast and Accurate Probing of In-Training LLMs' Downstream Performances
Zhichen Liu, Tianle Lun, Zhibin Wen +7
The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional ge…
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…
cs.CL2024★ 4 cited
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…