Large language models for information retrieval : challenges and chances

Abstract

The rapid advancement of Large Language Models (LLMs) has introduced a paradigm shift in Information Retrieval (IR), moving beyond conventional keyword queries and ranked result lists. LLMs now play a critical role in the evolution of IR technologies and introduce new interaction forms like Retrieval-Augmented Generation, which is a more dynamic and interactive retrieval process that integrates various aspects of Information Access, like Question Answering, into the dialog between a searcher and the search engine. We explore the multi-faceted impact of LLMs on IR, particularly in three distinct layers where they have become an integral part of the retrieval process, namely the retrieval system and processing pipeline that can make use of a richer semantic representation using advanced language models, the interaction layer, and the broader IR ecosystem. For the latter, we focus on evaluation issues as well as bias, fairness, and ethical concerns. We also highlight some recent cases of using LLMs in the medical domain to demonstrate the impact on one specific domain.