标题： 在多模态大语言模型中进行数据高效模型适应的基于基础的思维链引导 显示英文标题

标题： Bootstrapping Grounded Chain-of-Thought in Multimodal LLMs for Data-Efficient Model Adaptation

摘要： 多模态大语言模型（MLLMs）在使用自然语言解释图像方面表现出色。 然而，如果没有使用大规模数据集进行再训练，这些模型难以适应专业视觉任务，例如图表理解。 这个问题是由于预训练和下游数据集之间的不匹配造成的：预训练数据集主要集中在场景和物体上，但包含关于专业、非物体图像（如图表和表格）的信息有限。 在本文中，我们分享了一个有趣的发现，即使用思维链（CoT）推理数据训练MLLM可以促进模型在专业视觉任务中的适应，尤其是在数据有限的情况下。 然而，我们发现从预训练的MLLM中提炼出的CoT数据中存在一个关键问题，即数据中的推理步骤通常包含多个事实性错误。 为了解决这个问题，我们提出了基于接地的思维链（GCoT），这是一种简单的基于自举的方法，旨在将接地信息（即边界框）注入CoT数据中，从而使推理步骤更忠实于输入图像。 我们在五个专业视觉任务上评估了我们的方法，这些任务涵盖了包括图表、表格、收据和报告在内的多种视觉格式。 结果表明，在数据有限的情况下，我们的方法显著优于微调和蒸馏。 显示英文摘要

摘要： Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in interpreting images using natural language. However, without using large-scale datasets for retraining, these models are difficult to adapt to specialized vision tasks, e.g., chart understanding. This problem is caused by a mismatch between pre-training and downstream datasets: pre-training datasets primarily concentrate on scenes and objects but contain limited information about specialized, non-object images, such as charts and tables. In this paper, we share an interesting finding that training an MLLM with Chain-of-Thought (CoT) reasoning data can facilitate model adaptation in specialized vision tasks, especially under data-limited regimes. However, we identify a critical issue within CoT data distilled from pre-trained MLLMs, i.e., the data often contains multiple factual errors in the reasoning steps. To address the problem, we propose Grounded Chain-of-Thought (GCoT), a simple bootstrapping-based approach that aims to inject grounding information (i.e., bounding boxes) into CoT data, essentially making the reasoning steps more faithful to input images. We evaluate our approach on five specialized vision tasks, which cover a variety of visual formats including charts, tables, receipts, and reports. The results demonstrate that under data-limited regimes our approach significantly improves upon fine-tuning and distillation.