Legal Reasoning
Legal reasoning research focuses on developing computational models that can understand and emulate human legal reasoning, aiming to improve efficiency and fairness in legal processes. Current research heavily utilizes large language models (LLMs), often enhanced with retrieval-augmented generation (RAG) or fine-tuned on specialized legal datasets, to perform tasks like legal judgment prediction, question answering, and argumentation analysis. These advancements are significant because they offer the potential to automate time-consuming legal tasks, improve access to justice, and provide more transparent and consistent legal decision-making.
Papers
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?
Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje, Ishan Kavathekar, Bhaskara Hanuma Vedula, Gokul S Krishnan, Shreya Goyal, Anmol Goel, Balaraman Ravindran, Ponnurangam Kumaraguru
Chain of Logic: Rule-Based Reasoning with Large Language Models
Sergio Servantez, Joe Barrow, Kristian Hammond, Rajiv Jain