Document AI is a growing research field that focuses on the comprehension and extraction of information from scanned and digital documents to make everyday business operations more efficient. Numerous downstream tasks and datasets have been introduced to facilitate the training of AI models capable of parsing and extracting information from various document types such as receipts and scanned forms. Despite these advancements, both existing datasets and models fail to address critical challenges that arise in industrial contexts. Existing datasets primarily comprise short documents consisting of a single page, while existing models are constrained by a limited maximum length, often set at 512 tokens. Consequently, the practical application of these methods in financial services, where documents can span multiple pages, is severely impeded. To overcome these challenges, we introduce LongFin, a multimodal document AI model capable of encoding up to 4K tokens. We also propose the LongForms dataset, a comprehensive financial dataset that encapsulates several industrial challenges in financial documents. Through an extensive evaluation, we demonstrate the effectiveness of the LongFin model on the LongForms dataset, surpassing the performance of existing public models while maintaining comparable results on existing single-page benchmarks.

翻译：文档人工智能是一个新兴的研究领域，专注于理解和提取扫描及数字文档中的信息，以提升日常业务运营的效率。为促进能够解析和提取各类文档（如收据和扫描表单）信息的AI模型训练，研究者已引入多项下游任务及数据集。然而，尽管取得进展，现有数据集和模型均未能解决工业场景中的关键挑战。当前数据集主要由单页短文档构成，现有模型则受限于最大长度限制（通常为512个token），导致其在实际金融服务中处理多页文档的应用严重受限。为应对这些挑战，我们提出LongFin——一种能够编码高达4K token的多模态文档AI模型，并构建了LongForms数据集——一个涵盖金融文档多项工业挑战的综合金融数据集。通过广泛评估，我们验证了LongFin模型在LongForms数据集上的有效性，其在保持与现有单页基准测试相当性能的同时，超越了现有公开模型的性能。