Conversational Information Retrieval by Lever...

Conversational Information Retrieval by Leveraging Llms to Enhance User Experience

Author Name : Amit Raj, Kusum Sharma, Parineeta Jha

DOI: https://doi.org/10.56025/IJARESM.2025.1302252386

ABSTRACT A Conversational Information Retrieval (CIR) system can be defined as an information retrieval (IR) system characterized by a conversational interface that facilitates user interaction with the system to obtain information through multi-turn dialogues in natural language, whether in spoken or written modalities. Information Retrieval (IR) has undergone considerable transformation, transcending conventional search methodologies to address a wide array of user information requirements. IR models, LLMs, and human users establishes a novel technical paradigm that is significantly more effective for information seeking. IR models deliver timely and pertinent information, LLMs supply intrinsic knowledge, and humans assume a pivotal role as both demanders and assessors of the reliability of information services. Large Language Models (LLMs) have exhibited remarkable proficiency in text comprehension, generation, and knowledge inference, thereby unveiling promising prospects for research within the eld of IR. LLMs not only enhance the process of generative retrieval but also provide superior frameworks for user comprehension, model assessment, and user-system engagement. However, substantial challenges persist, encompassing computational expenses, issues of credibility, limitations specific to certain domains, and ethical implications.