On a Schiphol flight-information test set, knowledge-graph RAG (91.49%) beat SQL RAG (80.85%) and traditional RAG (84.84%) on accuracy and did far better on reasoning questions (68.75% vs 6.25% and 9.38%).
BM25 Query Augmentation Learned End-to-End
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abstract
Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation. We propose an approach to learning an augmentation and a re-weighting end-to-end, and we find that our approach improves performance over BM25 while retaining its speed. We furthermore find that the learned augmentations and re-weightings transfer well to unseen datasets.
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Evaluating the Performance of RAG Methods for Conversational AI in the Airport Domain
On a Schiphol flight-information test set, knowledge-graph RAG (91.49%) beat SQL RAG (80.85%) and traditional RAG (84.84%) on accuracy and did far better on reasoning questions (68.75% vs 6.25% and 9.38%).