215 citations · 893 across the 32 of their papers we have counts for
7 papers · 1 filter
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Fei Wang, Xingyu Fu, James Y. Huang +18
We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tas…
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs
Xueqing Wu, Zongyu Lin, Songyan Zhao +4
Visual programs are executable code generated by large language models to address visual reasoning problems. They decompose complex questions into multiple reasoning steps and invo…
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
Yihe Deng, Pan Lu, Fan Yin +6
Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understan…
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
Renrui Zhang, Dongzhi Jiang, Yichi Zhang +8
The remarkable progress of Multi-modal Large Language Models (MLLMs) has garnered unparalleled attention, due to their superior performance in visual contexts. However, their capab…
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data
Xiao Liu, Zirui Wu, Xueqing Wu +3
Quantitative reasoning is a critical skill to analyze data, yet the assessment of such ability remains limited. To address this gap, we introduce the Quantitative Reasoning with Da…
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
Dongyang Liu, Renrui Zhang, Longtian Qiu +16
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX…
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
Jia-Chen Gu, Hao-Xiang Xu, Jun-Yu Ma +4
Model editing is a technique that edits the large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining. While current mod…