LayoutLLM is a document understanding method based on large language models (LLMs) or multimodal LLMs (MLLMs) that incorporates layout instruction tuning to enhance the comprehension and utilization of document layouts. The core of LayoutLLM is a layout instruction tuning strategy, which includes two components: layout-aware pre-training and layout-aware supervised fine-tuning. During layout-aware pre-training, three pre-training tasks are introduced to capture document-level, region-level, and segment-level information. A novel module called LayoutCoT is designed to enable LayoutLLM to focus on relevant regions and generate accurate answers. LayoutCoT improves performance and provides interpretability for manual inspection and correction. Experiments on standard benchmarks show that LayoutLLM significantly outperforms existing methods that use open-source 7B LLMs/MLLMs for document understanding. LayoutLLM's contributions include three groups of pre-training tasks for layout-aware pre-training, a novel LayoutCoT strategy for layout-aware supervised fine-tuning, and experimental results demonstrating the effectiveness of LayoutLLM in zero-shot document understanding. The method is trained using layout instruction tuning, which consists of layout-aware pre-training and layout-aware supervised fine-tuning. The model architecture includes a document pre-trained model encoder, multimodal projectors, and an LLM. The layout instruction tuning strategy enhances the model's ability to understand document layouts by incorporating layout information into the training process. The method is evaluated on various document understanding benchmarks, showing significant improvements in performance. The ablation study confirms the effectiveness of layout-aware pre-training and layout-aware SFT in improving zero-shot document understanding. Qualitative results demonstrate that LayoutLLM can accurately focus on relevant areas, utilize layout information, and provide interpretability. Interactive correction with LayoutCoT is also shown to be effective in correcting errors. Despite its strengths, LayoutLLM has limitations in precisely understanding region-level relationships and needs further research to address these issues. Overall, LayoutLLM provides a more effective way to utilize layout information for document understanding, significantly improving the performance of zero-shot document understanding.LayoutLLM is a document understanding method based on large language models (LLMs) or multimodal LLMs (MLLMs) that incorporates layout instruction tuning to enhance the comprehension and utilization of document layouts. The core of LayoutLLM is a layout instruction tuning strategy, which includes two components: layout-aware pre-training and layout-aware supervised fine-tuning. During layout-aware pre-training, three pre-training tasks are introduced to capture document-level, region-level, and segment-level information. A novel module called LayoutCoT is designed to enable LayoutLLM to focus on relevant regions and generate accurate answers. LayoutCoT improves performance and provides interpretability for manual inspection and correction. Experiments on standard benchmarks show that LayoutLLM significantly outperforms existing methods that use open-source 7B LLMs/MLLMs for document understanding. LayoutLLM's contributions include three groups of pre-training tasks for layout-aware pre-training, a novel LayoutCoT strategy for layout-aware supervised fine-tuning, and experimental results demonstrating the effectiveness of LayoutLLM in zero-shot document understanding. The method is trained using layout instruction tuning, which consists of layout-aware pre-training and layout-aware supervised fine-tuning. The model architecture includes a document pre-trained model encoder, multimodal projectors, and an LLM. The layout instruction tuning strategy enhances the model's ability to understand document layouts by incorporating layout information into the training process. The method is evaluated on various document understanding benchmarks, showing significant improvements in performance. The ablation study confirms the effectiveness of layout-aware pre-training and layout-aware SFT in improving zero-shot document understanding. Qualitative results demonstrate that LayoutLLM can accurately focus on relevant areas, utilize layout information, and provide interpretability. Interactive correction with LayoutCoT is also shown to be effective in correcting errors. Despite its strengths, LayoutLLM has limitations in precisely understanding region-level relationships and needs further research to address these issues. Overall, LayoutLLM provides a more effective way to utilize layout information for document understanding, significantly improving the performance of zero-shot document understanding.