Bring Retrieval Augmented Generation to Google Gemini via External API: An Evaluation with BIG-Bench Dataset

Abstract The integration of Retrieval Augmented Generation (RAG) into existing large language models represents a significant shift towards more dynamic and context-aware AI systems. In this work, Google Gemini, a state-of-the-art language model, has been enhanced with RAG capabilities to leverage external, real-time data sources during the response generation process. This augmentation aims to address traditional limitations of language models, particularly in generating responses that require up-to-date information and adaptability to complex user queries. The performance of the RAG-enhanced Google Gemini was rigorously evaluated using the BIG-Bench dataset, which includes tasks designed to test the bounds of language models in terms of reasoning, contextuality, and factual accuracy. Quantitative results from this evaluation demonstrate marked improvements in accuracy and contextual relevance across various tasks, indicating the effectiveness of RAG in enhancing model performance. Qualitative assessments further support these findings, highlighting the model’s improved ability to generate precise and relevant responses. However, the integration of RAG also introduces challenges related to computational efficiency and scalability, emphasizing the need for further optimization. This paper discusses potential future research directions, including the application of RAG to other datasets, exploration of different RAG configurations, and the development of more sophisticated data handling techniques to enhance the model’s performance and applicability. The ongoing advancement of RAG technologies promises to significantly broaden the utility of AI-driven systems in real-world applications, making them more adaptable and useful across diverse and dynamic scenarios.