70 citations · 72 across the 4 of their papers we have counts for
4 papers
FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
Xusheng He, Wei Liu, Shanshan Ma +3
Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advanceme…
FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging
Zichen Tang, Haihong E, Jiacheng Liu +18
We present FinMMR, a novel bilingual multimodal benchmark tailored to evaluate the reasoning capabilities of multimodal large language models (MLLMs) in financial numerical reasoni…
SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex Multimodal Reasoning in Academic Areas
Chenghao Ma, Haihong E., Junpeng Ding +7
Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate impressive problem-solving skills in many tasks and domains. However, their ability to reason with compl…
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Haoran Luo, Haihong E, Zichen Tang +9
Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge…