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New adapter enables backward compatibility for multi-modal LLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed the Multi-modal Knowledge Preserving Adapter (MKP-Adapter), a novel approach for ensuring backward compatibility in multi-modal large language models (MLLMs) without needing to update the core backbone model. This adapter-only method addresses the challenge of preserving new embedding knowledge while maintaining compatibility, utilizing a multi-level preservation loss and a focal re-weighting strategy. Experiments show MKP-Adapter achieves strong backward compatibility across various multi-modal tasks, including image, text, visual document, and video retrieval, with minimal added latency. AI

IMPACT This method could significantly reduce the cost and complexity of updating embedding models in multi-modal AI systems.

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

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New adapter enables backward compatibility for multi-modal LLMs

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The cluster contains an academic paper detailing a new technical method for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jaeseok Byun, Gukyeong Kwon, Han-Kai Hsu, Meher Gitika Karumuri, Zhikang Zhang, Hao Yang, Davide Modolo ·

    Multi-modal Knowledge Preserving Adapter for Embedding Backward Compatibility

    arXiv:2609.16875v1 Announce Type: new Abstract: Upgrading embedding models typically requires expensive database re-indexing, as new query embeddings are incompatible with existing database embeddings. While Backward Compatible Training (BCT) mitigates this by enforcing compatibi…

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