International Conference on Information and Knowledge Technology

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Context Awareness Gate for Retrieval Augmented Generation

Authors :

Mohammad Hassan Heydari¹ Arshia Hemmat² Erfan Naman³ Afsaneh Fatemi⁴

1- دانشگاه اصفهان 2- دانشگاه اصفهان 3- دانشگاه اصفهان 4- دانشگاه اصفهان

Keywords :

Retrieval-Augmented Generation،Hallucination،Large Language Models،Open Domain Question Answering

Abstract :

Retrieval-augmented generation (RAG) has emerged as a widely adopted approach to mitigate the limitations of large language models (LLMs) in answering domain-specific questions. Previous research has predominantly focused on improving the accuracy and quality of retrieved data chunks to enhance the overall performance of the generation pipeline. However, despite ongoing advancements, the critical issue of retrieving irrelevant information—which can impair a model’s ability to utilize its internal knowledge effectively—has received minimal attention. In this work, we investigate the impact of retrieving irrelevant information in open-domain question answering, highlighting its significant detrimental effect on the quality of LLM outputs. To address this challenge, we propose the Context Awareness Gate (CAG) architecture, a novel mechanism that dynamically adjusts the LLM’s input prompt based on whether the user query necessitates external context retrieval. Additionally, we introduce the Vector Candidates method, a core mathematical component of CAG that is statistical, LLM-independent, and highly scalable. We further examine the distributions of relationships between contexts and questions, presenting a statistical analysis of these distributions. This analysis can be leveraged to enhance the con- text retrieval process in retrieval-augmented generation (RAG) systems.