Legal Text
Legal text analysis is a rapidly evolving field leveraging natural language processing (NLP) and large language models (LLMs) to improve efficiency and access within the legal system. Current research focuses on applying LLMs to tasks like legal intake, question answering, and argument generation, often employing transformer-based architectures and exploring methods to enhance accuracy and mitigate biases. This work holds significant implications for streamlining legal processes, improving access to justice, and advancing the understanding of how LLMs can be effectively applied to complex, nuanced domains like law.
Papers
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Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning
Yang Wu, Chenghao Wang, Ece Gumusel, Xiaozhong Liu
The PESQetarian: On the Relevance of Goodhart's Law for Speech Enhancement
Danilo de Oliveira, Simon Welker, Julius Richter, Timo Gerkmann
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