The review begins by providing a historical overview of the development of language models, highlighting the importance of attention mechanisms in transforming language models into large language models (LLMs). It then discusses the pros and cons of proprietary versus open-source LLMs, emphasizing the advantages of open-source models in terms of accessibility, transparency, and cost-effectiveness.
The review then delves into the specific details of various LLMs, including GPT, Claude, Gemini, LLaMA, Mistral, Falcon, and Grok-1. It examines their architectural features, pre-training data, and performance on various benchmarks.
The review then shifts its focus to vision models and multi-modal large language models (MM-LLMs). It introduces BLIP-2, which utilizes a Querying Transformer (Q-Former) to effectively bridge the gap between image and text encoders. The review also covers the Vision Transformer (ViT), Contrastive Language–Image Pre-training (CLIP), and early approaches to multi-modal information processing.
The review then delves into specific MM-LLMs, such as LLaVA, Kosmos-1 and Kosmos-2, MiniGPT4, and mPLUG-OWL. It examines their architectural designs, training strategies, and performance on various vision-language tasks.
The review also discusses the challenges associated with MM-LLMs, such as hallucinations and data bias, and explores potential solutions, including the use of reinforcement learning with AI feedback and hallucination detection modules.
Finally, the review touches on the importance of model evaluation and benchmarking, highlighting the various performance tasks and benchmarks used to assess the capabilities of LLMs and MM-LLMs.
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소스 콘텐츠 기반
arxiv.org
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