80 citations · 111 across the 12 of their papers we have counts for
12 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…
Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization
Wenjie Zheng, Qiming Xie, Jianfei Yu +6
Recent advances in multimodal depression recognition for clinical interviews (MDRC) have demonstrated the potential of AI systems by integrating textual, acoustic, and facial cues.…
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…
OlympicArena Medal Ranks: Who Is the Most Intelligent AI So Far?
Zhen Huang, Zengzhi Wang, Shijie Xia +1
In this report, we pose the following question: Who is the most intelligent AI model to date, as measured by the OlympicArena (an Olympic-level, multi-discipline, multi-modal bench…
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Zhen Huang, Zengzhi Wang, Shijie Xia +25
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…
Benchmarking Benchmark Leakage in Large Language Models
Ruijie Xu, Zengzhi Wang, Run-Ze Fan +1
Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often u…
MathPile: A Billion-Token-Scale Pretraining Corpus for Math
Zengzhi Wang, Xuefeng Li, Rui Xia +1
High-quality, large-scale corpora are the cornerstone of building foundation models. In this work, we introduce MathPile, a diverse and high-quality math-centric corpus comprising…
Ask Again, Then Fail: Large Language Models' Vacillations in Judgment
Qiming Xie, Zengzhi Wang, Yi Feng +1
We observe that current conversational language models often waver in their judgments when faced with follow-up questions, even if the original judgment was correct. This wavering…
MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis
Hongjie Cai, Nan Song, Zengzhi Wang +7
Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple…
Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study
Zengzhi Wang, Qiming Xie, Yi Feng +3
Recently, ChatGPT has drawn great attention from both the research community and the public. We are particularly interested in whether it can serve as a universal sentiment analyze…
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…