1 citations · 1 across the 2 of their papers we have counts for
5 papers
Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods
Wanru Zhao, Yihong Chen, Yuzhi Tang +6
Data curation is a critical yet under-explored area in large language model (LLM) training. Existing methods, such as data selection and mixing, operate in an offline paradigm, det…
Dynamic Expert Sharing: Decoupling Memory from Parallelism in Mixture-of-Experts Diffusion LLMs
Hao Mark Chen, Zhiwen Mo, Royson Lee +6
Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their int…
CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics
Wanru Zhao, Hongxiang Fan, Shell Xu Hu +3
Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collabora…
Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance
Zhican Wang, Guanghui He, Lingjun Gao +6
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, combining high-quality reconstruction with efficient rendering. It has been widely adopte…
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science
Xinna Lin, Siqi Ma, Junjie Shan +5
Pursuing artificial intelligence for biomedical science, a.k.a. AI Scientist, draws increasing attention, where one common approach is to build a copilot agent driven by Large Lang…