零样本，但代价是什么？揭示MILS的LLM-CLIP框架用于图像字幕生成的隐藏开销
Zero-Shot, But at What Cost? Unveiling the Hidden Overhead of MILS's LLM-CLIP Framework for Image Captioning
AI总结：
本研究揭示MILS零样本图像字幕框架因迭代细化过程产生巨大计算开销，首次量化其质量与成本权衡，为高效多模态模型设计提供关键见解。
摘要：
MILS（多模态迭代LLM求解器）是一个最近发表的框架，声称通过利用基于迭代的LLM-CLIP方法进行零样本图像字幕生成，“LLM无需任何训练即可看和听”。虽然这种MILS方法表现出良好的性能，但我们的调查揭示，这种成功伴随着隐藏的、巨大的计算成本，这源于其昂贵的多步细化过程。相比之下，BLIP-2和GPT-4V等替代模型通过简化的单遍方法取得了有竞争力的结果。我们假设MILS迭代过程中固有的显著开销可能削弱其实用优势，从而挑战了零样本性能可以在不产生沉重资源需求的情况下获得的说法。这项工作首次揭示并量化了MILS中输出质量与计算成本之间的权衡，为设计更高效的多模态模型提供了关键见解。
英文摘要：
MILS (Multimodal Iterative LLM Solver) is a recently published framework that claims "LLMs can see and hear without any training" by leveraging an iterative, LLM-CLIP based approach for zero-shot image captioning. While this MILS approach demonstrates good performance, our investigation reveals that this success comes at a hidden, substantial computational cost due to its expensive multi-step refinement process. In contrast, alternative models such as BLIP-2 and GPT-4V achieve competitive results through a streamlined, single-pass approach. We hypothesize that the significant overhead inherent in MILS's iterative process may undermine its practical benefits, thereby challenging the narrative that zero-shot performance can be attained without incurring heavy resource demands. This work is the first to expose and quantify the trade-offs between output quality and computational cost in MILS, providing critical insights for the design of more efficient multimodal models.
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