7 papers
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models
Yueqian Wang, Xiaojun Meng, Yifan Wang +2
With the growing research focus on multimodal dialogue systems, the capability for proactive interaction is gradually gaining recognition. As an alternative to conventional turn-by…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Yichun Yin, Wenyong Huang, Kaikai Song +49
We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field…
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process
Jianxin Liang, Xiaojun Meng, Huishuai Zhang +3
Video Question Answering (VideoQA) is a challenging task that requires understanding complex visual and temporal relationships within videos to answer questions accurately. In this…
CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theory
Haokun Zhao, Jinyi Han, Jiaqing Liang +3
Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improve…
Friends-MMC: A Dataset for Multi-modal Multi-party Conversation Understanding
Yueqian Wang, Xiaojun Meng, Yuxuan Wang +3
Multi-modal multi-party conversation (MMC) is a less studied yet important topic of research due to that it well fits real-world scenarios and thus potentially has more widely-used…