36 citations · 56 across the 9 of their papers we have counts for
10 papers
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.…
OmniMatBench: A Human-Calibrated Multimodal Reasoning Benchmark Across 19 Materials Science Subfields
Wanhao Liu, Jiaqing Xie, Qian Tan +10
As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdisciplinary, multimodal, and…
MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation
Yang Han, Pengyu Wang, Kai Yu +2
Mass spectrometry (MS) plays a critical role in molecular identification, significantly advancing scientific discovery. However, structure elucidation from MS data remains challeng…
ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge
Zihan Zhao, Ziping Wan, Lu Chen +12
Atomized chemical knowledge, such as functional group information of molecules and reactions, plays a pivotal intermediate role in the reasoning process that connects molecular str…
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
Situo Zhang, Hanqi Li, Lu Chen +7
Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rather than transferable chemical reasoning…
From Generalist to Specialist: A Survey of Large Language Models for Chemistry
Yang Han, Ziping Wan, Lu Chen +2
Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining…
SciDFM: A Large Language Model with Mixture-of-Experts for Science
Liangtai Sun, Danyu Luo, Da Ma +7
Recently, there has been a significant upsurge of interest in leveraging large language models (LLMs) to assist scientific discovery. However, most LLMs only focus on general scien…
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…
Developing ChemDFM as a large language foundation model for chemistry
Zihan Zhao, Da Ma, Lu Chen +11
Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require tra…