33 citations · 172 across the 29 of their papers we have counts for
9 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…
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Yuxuan Sun, Yunlong Zhang, Yixuan Si +7
Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Sl…
How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?
Siye Wu, Jian Xie, Jiangjie Chen +3
By leveraging the retrieval of information from external knowledge databases, Large Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge-intensive…
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Kai Zhang, Yi Luan, Hexiang Hu +5
Image retrieval, i.e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-ba…
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
Yizhi LI, Ge Zhang, Xingwei Qu +16
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-fol…
TravelPlanner: A Benchmark for Real-World Planning with Language Agents
Jian Xie, Kai Zhang, Jiangjie Chen +5
Planning has been part of the core pursuit for artificial intelligence since its conception, but earlier AI agents mostly focused on constrained settings because many of the cognit…
UMIE: Unified Multimodal Information Extraction with Instruction Tuning
Lin Sun, Kai Zhang, Qingyuan Li +1
Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-speci…
Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning
Tinghui Zhu, Kai Zhang, Jian Xie +1
Recent advancements have significantly augmented the reasoning capabilities of Large Language Models (LLMs) through various methodologies, especially chain-of-thought (CoT) reasoni…
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Yuxuan Sun, Hao Wu, Chenglu Zhu +11
The emergence of large multimodal models has unlocked remarkable potential in AI, particularly in pathology. However, the lack of specialized, high-quality benchmark impeded their…