Intention and Context Elicitation with Large Language Models in the
  Legal Aid Intake Process
Nick Goodson Rongfei Lu
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.