New framework enables zero-shot embedding from multimodal LLMs

Researchers have developed a novel framework to adapt generative Multimodal Large Language Models (MLLMs) into effective embedding models without requiring extensive pre-training. This approach utilizes a hierarchical embedding prompt to condition the model and unlock zero-shot embedding capabilities. The system also incorporates a Self-aware Hard Negative Sampling (SaHa) technique, which rigorously filters out semantic false negatives by mapping retrieved candidates back to their owner queries, thereby maximizing intra-task discrimination and batch efficiency. AI

IMPACT This research offers a more data-efficient method for creating powerful multimodal embedding models, potentially reducing training costs and improving accessibility.

RANK_REASON The cluster contains a research paper detailing a new method for adapting multimodal LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Hugging Face
• Massive Multimodal Embedding Benchmark
• Multimodal Large Language Models
• Self-aware Hard Negative Sampling
• Yeong-Joon Ju

• paper
• model release

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