Are Re-Ranking in Retrieval-Augmented Generation Methods Impactful for Small Agriculture QA Datasets? A Small Experiment

Abstract

Agriculture requires accurate, location-specific information that would need the power of advanced Retrieval-Augmented Generation (RAG) models. To this end, we perform an experimental analysis on how integrating re-ranking strategies and in-memory computing into RAG models might affect performance on small agriculture question-answering (QA) datasets. This method envisages to enable real-time ground-truth kind of answers for agro-informatics sake, the proposed approach is to assist enhance document relevance and lower response latency. We trained the system on a large-scale agriculture QA dataset using high-level components like the Sentence Transformer for embedding generation, FAISS for fast vector search and a pre-trained language model for response generation. This is to keep the documents returned highly relevant, and zero-shot classification was used for re-ranking techniques. The efficacy of their algorithm across a range of QDMR transformation tasks was evaluated, and the experiment evaluation showed that rereading did not significantly increase performance over baselines. But the in-memory computing with FAISS greatly reduced retrieval latency which makes it appropriate for real-time applications in agriculture QA systems.