16 citations · 18 across the 11 of their papers we have counts for
11 papers
DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents
Yubin Wang, Xingjian Wei, Jiang Wu +32
High-quality structured organic reaction data are essential for developing artificial intelligence for chemistry (AI4Chem), yet much of this knowledge remains dispersed across pate…
MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition
Haote Yang, Jiang Wu, Jingchao Wang +42
In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagram…
Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing
Jiahe Song, Chuang Wang, Yinfan Wang +11
Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promisin…
NMRTrans: Structure Elucidation from Experimental NMR Spectra via Set Transformers
Liujia Yang, Zhuo Yang, Jiaqing Xie +9
Nuclear Magnetic Resonance (NMR) spectroscopy is fundamental for molecular structure elucidation, yet interpreting spectra at scale remains time-consuming and highly expertise-depe…
RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning
Jiahe Song, Chuang Wang, Bowen Jiang +13
Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machi…
GTR-CoT: Graph Traversal as Visual Chain of Thought for Molecular Structure Recognition
Jingchao Wang, Yifan He, Haote Yang +10
Optical Chemical Structure Recognition (OCSR) is essential for converting molecular images into machine-readable formats. While recent vision-language models (VLMs) have shown prom…
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models
Xinlin Zhuang, Jiahui Peng, Ren Ma +7
The composition of pre-training datasets for large language models (LLMs) remains largely undisclosed, hindering transparency and efforts to optimize data quality, a critical drive…
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
Haote Yang, Xingjian Wei, Jiang Wu +18
We introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. OpenHuEval is constructed from a vast collection of Hungarian-specific mater…
Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning
Junyuan Gao, Jiahe Song, Jiang Wu +9
Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether…
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
Qingyun Li, Zhe Chen, Weiyun Wang +37
Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resem…
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Zhe Chen, Weiyun Wang, Hao Tian +32
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…