用多模态大模型解析法律文件图像，帮普通人自动提取信息
Analyzing Images of Legal Documents: Toward Multi-Modal LLMs for Access to Justice
- 将手写纸质表单图像输入多模态大模型，实现信息自动结构化提取
- 初步结果显示在高质量图像下可有效识别关键字段，但低质量图像表现受限
- 适合法律援助者、自诉人及需要简化政务流程的普通用户
与法律系统和政府机构互动需要收集并分析分散在各类文档中的信息，如表格、证书和合同（例如租约）。这些信息对于理解自身法律权利，以及填写诉讼或申请政府福利的表格至关重要。然而，对普通人而言，查找正确信息、定位合适表格并准确填写仍具挑战性。大型语言模型（LLMs）虽有潜力缓解这一问题，但仍依赖用户手动提供信息，若信息仅存在于复杂纸质文档中，则易出错。本文研究利用多模态LLM分析手写纸质表单的图像，以自动提取相关信息并生成结构化数据。初步结果表明该方法具有前景，但存在局限性（如图像质量较低时性能下降）。本工作展示了集成多模态LLM支持普通人及自诉人在获取和整合法律信息方面的潜力。
原文摘要 · Abstract (English)
Interacting with the legal system and the government requires the assembly and analysis of various pieces of information that can be spread across different (paper) documents, such as forms, certificates and contracts (e.g. leases). This information is required in order to understand one's legal rights, as well as to fill out forms to file claims in court or obtain government benefits. However, finding the right information, locating the correct forms and filling them out can be challenging for laypeople. Large language models (LLMs) have emerged as a powerful technology that has the potential to address this gap, but still rely on the user to provide the correct information, which may be challenging and error-prone if the information is only available in complex paper documents. We present an investigation into utilizing multi-modal LLMs to analyze images of handwritten paper forms, in order to automatically extract relevant information in a structured format. Our initial results are promising, but reveal some limitations (e.g., when the image quality is low). Our work demonstrates the potential of integrating multi-modal LLMs to support laypeople and self-represented litigants in finding and assembling relevant information.
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