A Retrieval-Augmented Generation Based Large Language Model Benchmarked on a Novel Dataset
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Modular RAG: Optimizing LLMs for Indigenous Knowledge Preservation This research paper explores a Retrieval-Augmented Generation (RAG) framework for large language models (LLMs). The study uses a novel dataset of interviews with Amazon rainforest natives and biologists to assess the impact of different RAG components (base language models like GPT and Palm, similarity scoring algorithms) on performance. The modular RAG design allows for interchangeable components, enabling the investigation of various configurations. Results show that model performance varies depending on the combination of components and whether contextual data is included; specifically, optimal performance is achieved when models are paired with similarity scores from their native platforms. The findings suggest that RAG offers a more efficient alternative to traditional LLM fine-tuning, with implications for both LLM development and the preservation of indigenous knowledge. Ref https://www.researchgate.net/publication/378449219_A_Retrieval-Augmented_Generation_Based_Large_Language_Model_Benchmarked_On_a_Novel_Dataset
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