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The use of subtle and sophisticated language in the legal industry makes it difficult to create automated systems that can produce pertinent inquiries. With its capacity to learn from interactions with the environment, reinforcement learning offers a viable solution to this issue. The system learns to produce accurate and contextually relevant questions by exposing an RL agent to a corpus of legal texts and expert comments. Legal experts, researchers, and students will benefit greatly from the RL-based approach’s potential to automate the production of pertinent legal questions for their information retrieval and analysis activities. The proposed approach is done using REINFORCE algorithm to maximize reward and generate questions accurately from the given context.

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Automating Legal Inquiry: A Reinforcement Learning Approach to Query Generation

  • 会议论文

  • 发表日期：

    2025年

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  • Baddireddi Sree Chandana,
  • Chakrika Kommana,
  • G. Sethu Madhav,
  • J. Amudha,
  • D. Radha

摘要

The use of subtle and sophisticated language in the legal industry makes it difficult to create automated systems that can produce pertinent inquiries. With its capacity to learn from interactions with the environment, reinforcement learning offers a viable solution to this issue. The system learns to produce accurate and contextually relevant questions by exposing an RL agent to a corpus of legal texts and expert comments. Legal experts, researchers, and students will benefit greatly from the RL-based approach’s potential to automate the production of pertinent legal questions for their information retrieval and analysis activities. The proposed approach is done using REINFORCE algorithm to maximize reward and generate questions accurately from the given context.