367 citations
- Tianjin University of TechnologyCN18 papers
- Tianjin UniversityCN15 papers
- Tsinghua UniversityCN8 papers
- Tianjin Foreign Studies UniversityCN4 papers
- Chinese Academy of SciencesCN3 papers
- Kavli Institute for Theoretical SciencesCN3 papers
- Nankai UniversityCN3 papers
- Fudan UniversityCN2 papers
- Jeonbuk National UniversityKR2 papers
- Jilin UniversityCN2 papers
- Mokpo National UniversityKR2 papers
- Nanjing UniversityCN2 papers
46 papers
A Counterexample to a Problem of Pommerenke on Convex Functions in the Class
Yuankai Guo, Xiaozhe Hu
Let denote the class of functions that are analytic and univalent in the exterior unit disk . In 19…
Beyond Value Benchmarks: Measuring Value-Structure Alignment in Large Language Models via Symmetric Q-Sorts
Jingting Zheng, Yuqi Ren, Linhao Yu +2
Large Language Models (LLMs) are increasingly deployed in contexts requiring complex moral reasoning and value trade-offs. However, existing evaluations typically rely on item-leve…
MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation
Bo Li, Ningyuan Deng, Tianyu Dong +3
Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images cruci…
Data Mixing for Large Language Models Pretraining: A Survey and Outlook
Zhuo Chen, Yuxuan Miao, Supryadi +1
Large language models (LLMs) rely on pretraining on massive and heterogeneous corpora, where training data composition has a decisive impact on training efficiency and downstream g…
An investigation on the FWHM of absorption features of type Ia supernovae
Xulin Zhao, Keiichi Maeda, Xiaofeng Wang
We present an investigation of the full width at half maximum (FWHM, or γ) of absorption features of Type Ia supernova (SNe Ia). We found that, the average value of FWHM can be wel…
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
Haoqian Meng, Yilun Luo, Yafei Zhao +3
The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing…