Large Language Models (LLMs) and chatbots show significant promise in streamlining the legal intake process. This advancement can greatly reduce the workload and costs for legal aid organizations, improving availability while making legal assistance more accessible to a broader audience. However, a key challenge with current LLMs is their tendency to overconfidently deliver an immediate 'best guess' to a client's question based on the output distribution learned over the training data. This approach often overlooks the client's actual intentions or the specifics of their legal situation. As a result, clients may not realize the importance of providing essential additional context or expressing their underlying intentions, which are crucial for their legal cases. Traditionally, logic based decision trees have been used to automate intake for specific access to justice issues, such as immigration and eviction. But those solutions lack scalability. We demonstrate a proof-of-concept using LLMs to elicit and infer clients' underlying intentions and specific legal circumstances through free-form, language-based interactions. We also propose future research directions to use supervised fine-tuning or offline reinforcement learning to automatically incorporate intention and context elicitation in chatbots without explicit prompting.

翻译：大语言模型（LLMs）和聊天机器人在简化法律受理流程方面展现出显著前景。这一进步可大幅减轻法律援助机构的工作负担与成本，在提升服务可用性的同时，使法律帮助更易于惠及更广泛的群体。然而，当前大语言模型面临的关键挑战在于：模型倾向于基于训练数据习得的输出分布，过度自信地对客户问题直接给出即时“最佳猜测”。这种方法常忽视客户的实际意图或其法律案件的具体情况。因此，客户可能未能意识到提供至关重要的附加背景信息或表达潜在意图的重要性——而这些对其法律案件至关重要。传统上，基于逻辑的决策树已被用于自动化处理特定司法救济问题（如移民和驱逐案件）的受理，但此类解决方案缺乏可扩展性。我们通过概念验证，展示了利用大语言模型通过自由形式的语言交互来引导并推断客户潜在意图及具体法律情境的能力。此外，我们提出未来研究方向：通过监督微调或离线强化学习，在聊天机器人中无需显式提示即可自动整合意图与背景的提取。