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cs.IR2026
MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
Haohang Huang, Xuan Lu, Mingyi Su +9
Multimodal embedding models aim to map heterogeneous inputs, such as text, images, videos, and audio, into a shared semantic space. However, existing methods and benchmarks remain…
cs.IR2024
Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models
Jianqun Zhou, Yuanlei Zheng, Wei Chen +5
Instruction-following capabilities in LLMs have progressed significantly, enabling more complex user interactions through detailed prompts. However, retrieval systems have not matc…