36 citations · 62 across the 32 of their papers we have counts for
4 papers · 1 filter
Hierarchical Data Selection via Manifold Coverage and Sparse Feature Coverage in LLM Post-training
Peng Sun, Yi Yang, Antong Zhang +7
As supervised fine-tuning data continues to scale, selecting high-value subsets from large candidate pools is crucial for reducing training cost and improving model performance. Ex…
Data-DPO: Direct Preference Optimization for Target Model Data Selection in LLM Post-Training
Peng Sun, Yi Yang, Antong Zhang +7
Data selection in supervised fine-tuning aims to select a small set of effective samples from large-scale candidate data, reducing training cost while preserving model performance.…
Task-Specific Data Selection for Instruction Tuning via Monosemantic Neuronal Activations
Da Ma, Gonghu Shang, Zhi Chen +6
Instruction tuning improves the ability of large language models (LLMs) to follow diverse human instructions, but achieving strong performance on specific target tasks remains chal…
ChemDFM-X: Towards Large Multimodal Model for Chemistry
Zihan Zhao, Bo Chen, Jingpiao Li +10
Rapid developments of AI tools are expected to offer unprecedented assistance to the research of natural science including chemistry. However, neither existing unimodal task-specif…