Exploring Opportunities for Synergies in IR and NLP through LLMs in Legal Context

Information Retrieval (IR) and Natural Language Processing (NLP) have historically evolved as distinct disciplines, each developing its own methodologies and addressing different challenges.

IR has been primarily focused on refining methods for effective document-query relevance, leveraging statistical and increasingly complex semantic approaches.

Meanwhile, NLP has seen a different trajectory, evolving from foundational language analysis techniques such as n-gram frequencies to more advanced strategies, culminating in the development of Large Language Models (LLMs).

The unprecedented capabilities of these models in language understanding and generation present new opportunities for synergy of NLP and IR, indicating a complementary relationship ripe for exploration.

While IR systems benefit from more nuanced and contextaware retrieval capabilities of LLMs, the integration of IR techniques into LLMs addresses their intrinsic challenges, improving their trustworthiness and reducing hallucinations.

This proposal outlines a set of research directions at the intersection of these two fields, mainly focusing on the use of IR to improve the performance and reliability of LLMs.