参数高效微调：让多模态大语言模型更强大

近年来，多模态大语言模型（MLLMs）的出现彻底改变了多模态学习的格局。这些模型，例如LLaVA、MiniGPT4和GPT4-Vision，在各种多模态任务中展现出令人惊叹的能力。然而，由于MLLMs通常包含数十亿个参数，对所有参数进行微调变得非常具有挑战性。

为了解决这个问题，本文研究了针对MLLMs的参数高效微调（PEFT）方法。我们的目标是在仅训练少量参数的情况下，找到提高MLLMs性能的有效方法。

参数高效微调：微调的艺术

传统的参数微调方法需要对模型的所有参数进行训练，这对于大型模型来说成本高昂且耗时。而PEFT方法则通过只训练模型中一小部分参数来实现高效的微调。

本文研究了四种常用的PEFT方法：LoRA、IA3、Adapter和Prefix-Tuning。这些方法通过不同的方式在模型中添加可训练参数，从而在保持模型整体结构不变的情况下，提升模型在特定任务上的表现。

连接层：多模态的关键

与单模态LLMs不同，MLLMs引入了额外的模块：视觉编码器和连接层。连接层负责将视觉信息与文本信息进行融合，并将融合后的信息传递给LLM进行处理。

本文重点研究了连接层在PEFT中的作用。我们发现，对连接层进行微调通常可以提高MLLMs在各种多模态任务上的性能。

实验结果：PEFT方法大比拼

为了评估不同PEFT方法的性能，我们对三个包含连接层的MLLMs进行了实验：LLaVA-1.5（7B. ��13B）、ShareGPTv4（7B）和Qwen-VL-Chat（7B）。实验结果表明：✅

• Adapter方法在所有方面都表现最佳，包括准确率、稳定性、泛化能力和减少幻觉。
• LoRA方法在大多数情况下表现良好，紧随Adapter之后。
• 对连接层进行微调通常可以提高MLLMs的性能，尤其是在处理未见过的数据集时。

探索PEFT的奥秘

除了评估不同PEFT方法的性能，我们还对PEFT方法的一些关键问题进行了深入研究：

• PEFT模块的位置： 我们发现，将PEFT模块放置在多头注意力层和MLP层中可以获得最佳性能。
• 训练数据规模： 训练数据规模越大，PEFT方法的性能越好。然而，当资源有限时，可以考虑使用中等规模的数据集。
• 模型稳定性： 我们发现，Adapter和LoRA在稳定性方面表现出显著差异。Adapter在处理已见过的数据集时，随着可训练参数的减少而变得更加稳定；而在处理未见过的数据集时，则相反。LoRA在处理已见过的数据集时，随着可训练参数的减少而变得更加不稳定；而在处理未见过的数据集时，则相反。
• 过拟合和泛化： 我们发现，Adapter和LoRA在抵抗过拟合方面表现出更强的鲁棒性。Adapter在泛化能力方面表现最佳，而Prefix-Tuning在泛化能力方面表现最差。
• 幻觉： 我们发现，Adapter方法在减少幻觉方面表现最佳。

未来展望

本文的研究表明，PEFT方法是提高MLLMs性能的一种有效方法。未来，我们将继续探索PEFT方法的潜力，并研究如何将PEFT方法应用于更多类型的MLLMs和多模态任务。

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