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From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model
🤖AI Summary
Researchers propose a data-efficient framework to convert generative Multimodal Large Language Models into universal embedding models without extensive pre-training. The method uses hierarchical embedding prompts and Self-aware Hard Negative Sampling to achieve competitive performance on embedding benchmarks using minimal training data.
Key Takeaways
- →New framework bypasses resource-intensive contrastive pre-training typically required for MLLM adaptation.
- →Hierarchical embedding prompts provide strong latent conditioning to bridge modality gaps.
- →Self-aware Hard Negative Sampling filters semantic false negatives by mapping candidates back to owner queries.
- →Method achieves competitive fine-tuning performance using only a fraction of standard training data.
- →Approach unlocks zero-shot embedding capabilities in multimodal language models.
#multimodal-llm#embedding-models#zero-shot-learning#machine-learning#ai-research#data-efficiency#model-adaptation
Read Original →via arXiv – CS AI
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