International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
RETRIEVAL-AUGMENTED GENERATION AND LONG CONTEXT MODELS: A COMPARATIVE ANALYSIS OF ADVANCED GENERATIVE AI APPROACHES Lalith Kumar Maddali BrightEdge, USA ----------------------------------------------------------------------***----------------------------------------------------------------------ABSTRACT In recent years, generative AI models have made significant advancements with the introduction of cutting-edge techniques like Retrieval-Augmented Generation (RAG) and Long Context Models. Through the analysis of extensive data and the generation of tailored results, these models aim to enhance the efficiency and capabilities of AI systems.
This article provides a comparison between the RAG and Long Context Models, discussing their architectures, advantages, disadvantages, and potential applications. RAG models have the ability to access a wide array of up-to-date information and integrate it into the outputs they generate. This is achieved by combining extensive language models with external knowledge retrieval [1]. The quality and reliability of external sources, however, may impose limitations on them [2]. Alternatively, longer text sequences can be effectively processed and remembered by Long Context Models, such as Transformers with extended attention mechanisms. These models are able to maintain coherence and consistency over lengthy passages [3]. Nevertheless, dealing with lengthy texts can pose challenges for these models, necessitating substantial computational resources [4]. The paper explores the applications of hybrid models and the potential benefits of combining RAG and Long Context approaches [5]. Finally, potential future directions for this field include the creation of innovative hybrid models, improvements to retrieval mechanisms, and advancements in memory and processing. In order to enhance the potency and effectiveness of generative AI systems, it is crucial to understand the pros and cons of RAG and Long Context Models. Keywords: Generative AI models, Retrieval-Augmented Generation (RAG), Long Context Models, External knowledge retrieval, Hybrid AI approaches
INTRODUCTION The development of machine-generated text with remarkable coherence and fluency has been made possible by generative AI models, revolutionizing the field of natural language processing. These models have been applied in various fields, including machine translation, dialogue systems, content creation, and summarization [6]. The increasing demand
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