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20242026
most citedChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

180 citations · 181 across the 13 of their papers we have counts for

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5 papers · 1 filter

cs.AI2024

VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning

Zhihuan Jiang, Zhen Yang, Jinhao Chen +4

Multi-modal large language models (MLLMs) have demonstrated promising capabilities across various tasks by integrating textual and visual information to achieve visual understandin…

cs.CL2024

MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model

Zhen Yang, Jinhao Chen, Zhengxiao Du +6

Large language models (LLMs) have demonstrated significant capabilities in mathematical reasoning, particularly with text-based mathematical problems. However, current multi-modal…

cs.CL2024180 cited

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Team GLM, :, Aohan Zeng +56

We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report primarily focuses on the GLM-4 language series, which includes…

cs.LG20241 cited

Does Negative Sampling Matter? A Review with Insights into its Theory and Applications

Zhen Yang, Ming Ding, Tinglin Huang +5

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processi…

cs.IR2024

TriSampler: A Better Negative Sampling Principle for Dense Retrieval

Zhen Yang, Zhou Shao, Yuxiao Dong +1

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While exist…