most citedMathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data

3 citations · 3 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CV2025

NeMo: Needle in a Montage for Video-Language Understanding

Zi-Yuan Hu, Shuo Liang, Duo Zheng +10

Recent advances in video large language models (VideoLLMs) call for new evaluation protocols and benchmarks for video-language understanding. Inspired by the needle in a haystack t…

cs.CL2025

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining

Zhixun Chen, Ping Guo, Wenhan Han +10

Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scal…

cs.CL2025

MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages

Wenhan Han, Yifan Zhang, Zhixun Chen +7

Multilingual large language models (LLMs) are advancing rapidly, with new models frequently claiming support for an increasing number of languages. However, existing evaluation dat…

cs.CL20243 cited

MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data

Meng Fang, Xiangpeng Wan, Fei Lu +2

Large language models (LLMs) have significantly advanced natural language understanding and demonstrated strong problem-solving abilities. Despite these successes, most LLMs still…

cs.CL2024

LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing

Jiangshu Du, Yibo Wang, Wenting Zhao +37

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and ques…