5 citations · 6 across the 3 of their papers we have counts for
4 papers
MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning
Run-Ze Fan, Zengzhi Wang, Pengfei Liu
Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. However, the open-source com…
MegaMath: Pushing the Limits of Open Math Corpora
Fan Zhou, Zengzhi Wang, Nikhil Ranjan +5
Mathematical reasoning is a cornerstone of human intelligence and a key benchmark for advanced capabilities in large language models (LLMs). However, the research community still l…
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
Fan Zhou, Zengzhi Wang, Qian Liu +2
Large language model pre-training has traditionally relied on human experts to craft heuristics for improving the corpora quality, resulting in numerous rules developed to date. Ho…
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning
Zengzhi Wang, Rui Xia, Jianfei Yu
Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level sentiment information. There are many ABSA tasks, and the current dominant paradigm is to train tas…