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How does the zero-shot cross-domain retrieval performance of MMICL compare to specialized multimodal models on

Authors: SOVEREIGN Research Kernel;

How does the zero-shot cross-domain retrieval performance of MMICL compare to specialized multimodal models on

Abstract

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large Language Models (LLMs), along with the emerging field of Multimodal Large Language Models (MLLMs), have demonstrated impressive capabilities across a wide range of multimodal tasks and applications. Particularly, various MLLMs, each with distinct model architectures, training data, and training stages, have been evaluated across a broad range of MLLM benchmarks. These studies have, to varying degrees, revealed differResearch goal: How does the zero-shot cross-domain retrieval performance of MMICL compare to specialized multimodal models on TextCaps when evaluated using precision@K and mean average precision (mAP) metrics?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.

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