Empirical Legal

Empirical legal studies are leveraging large language models (LLMs) to address challenges in legal analytics, such as case retrieval, precedent prediction, and legal text classification. Current research focuses on adapting and fine-tuning LLMs like GPT and Llama for specific legal tasks, often employing techniques like in-context learning and few-shot learning to improve efficiency and accuracy. This work aims to automate time-consuming tasks, improve the accessibility of legal information, and enhance the rigor of empirical legal research through more efficient data processing and analysis. The resulting advancements have significant implications for legal professionals, researchers, and policymakers by streamlining legal processes and providing new insights into legal trends and patterns.