Long Context RAG: Unlocking the Potential of Large Language Models for Complex Queries

Abstract

Retrieval-augmented generation (RAG) is a breakthrough in AI and has extended the capability to answer complex, knowledge-based questions. As a result of integrating functionalities of retrieval systems with generative large language models (LLMs), RAG systems offer the flexibility of complex responses to queries that demand integration of multiple sources. The traditional RAG systems perform well for open domain question answering and knowledge-base QA tasks. Still, the performance degrades for the long context queries requiring the model to understand it in terms of several domains and interconnect with each other.

This paper discusses whether deeper contextual processing of the referred advanced LLMs can help enrich the existing RAG paradigm. Consequently, key contributions involve formulating current deficiencies in processing contextually extensive questions, exploring various ways of context retention integration, and assessing the superiority of refined RAG systems in actual-world domains. Based on a thorough assessment of the applicability of the proposed context-aware RAG system, this paper discusses the ability of the technology to bring about nothing short of a disruptive shift in industries including healthcare LegalTech, as well as academic scholarship. Pausing the conclusions deriving from the presented study are that while the fine-tuned RAG systems significantly help to upgrade the scales of responses’ relevancy and pushing the answers with more accurate approaches to queries, they provide the solutions to the society’s growing requirement for the management of the complex inquiries.