3 citations · 3 across the 8 of their papers we have counts for
8 papers
CausalChapter: Improving Long-Video Chaptering with Interventional Dependency Modeling
Xinran Duan, Guozhang Li, Yaoyao Zhong +3
Long-form instructional videos require automatic chaptering to support browsing, navigation, and knowledge access. Recent long-context language models can perform chaptering from t…
SketchJudge: A Diagnostic Benchmark for Grading Hand-drawn Diagrams with Multimodal Large Language Models
Yuhang Su, Mei Wang, Yaoyao Zhong +4
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in visual understanding, they often struggle when faced with the unstructured and ambiguous nature…
Discerning minds or generic tutors? Evaluating instructional guidance capabilities in Socratic LLMs
Ying Liu, Can Li, Ting Zhang +4
The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. While prior research has primarily examined their…
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
NVIDIA, :, Aarti Basant +214
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…
From Answers to Questions: EQGBench for Evaluating LLMs' Educational Question Generation
Chengliang Zhou, Mei Wang, Ting Zhang +3
Large Language Models (LLMs) have demonstrated remarkable capabilities in mathematical problem-solving. However, the transition from providing answers to generating high-quality ed…
VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMs
Can Li, Ying Liu, Ting Zhang +2
Large Multimodal Models have achieved remarkable progress in integrating vision and language, enabling strong performance across perception, reasoning, and domain-specific tasks. H…
Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning
Xuetao Ma, Wenbin Jiang, Hua Huang
In-context learning (ICL) can significantly enhance the complex reasoning capabilities of large language models (LLMs), with the key lying in the selection and ordering of demonstr…
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
Zheqi He, Xinya Wu, Pengfei Zhou +5
Multi-modal large language models(MLLMs) have achieved remarkable progress and demonstrated powerful knowledge comprehension and reasoning abilities. However, the mastery of domain…