视觉大模型中更大的编码器是否总是更好？
Are Bigger Encoders Always Better in Vision Large Models?
AI总结：
本文通过在MLLMs预训练阶段使用不同编码器和LLM尺寸进行实验，发现仅增大编码器并不一定能提升视觉语言模型性能，并分析了LLM规模与数据质量的影响及扩展规律差异。
摘要：
近年来，多模态大语言模型（MLLMs）在现实应用中展现出强大潜力。由于它们具有理解多模态信息的卓越能力以及固有的强大认知和推理能力，这些模型正在快速发展。在MLLMs中，视觉语言模型（VLM）因其理解视觉信息的能力而脱颖而出。然而，当前主流范式下VLM的扩展趋势尚未得到广泛研究。通过训练更大的模型是否能够获得更好的性能仍不清楚。为了解决这一问题，我们在MLLMs的预训练阶段进行了实验。我们使用不同的编码器尺寸和大语言模型（LLM）尺寸进行实验。我们的研究结果表明，仅仅增加编码器的尺寸并不一定能提升VLM的性能。此外，我们分析了LLM骨干参数规模和数据质量对预训练结果的影响。我们还探讨了LLM与VLM之间扩展规律的差异。
英文摘要：
In recent years, multimodal large language models (MLLMs) have shown strong potential in real-world applications. They are developing rapidly due to their remarkable ability to comprehend multimodal information and their inherent powerful cognitive and reasoning capabilities. Among MLLMs, vision language models (VLM) stand out for their ability to understand vision information. However, the scaling trend of VLMs under the current mainstream paradigm has not been extensively studied. Whether we can achieve better performance by training even larger models is still unclear. To address this issue, we conducted experiments on the pretraining stage of MLLMs. We conduct our experiment using different encoder sizes and large language model (LLM) sizes. Our findings indicate that merely increasing the size of encoders does not necessarily enhance the performance of VLMs. Moreover, we analyzed the effects of LLM backbone parameter size and data quality on the pretraining outcomes. Additionally, we explored the differences in scaling laws between LLMs and VLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
