The integration of ANN indices in information retrieval systems causes a significant deterioration in retrieval quality when compared to traditional brute-force methods. Evaluation metrics reveal that recall@100 drops over 10 points across various baseline models once the ANN index is introduced. Furthermore, the performance ranking of models shifts, with SimANS underperforming compared to ANCE in some recall metrics. This disparity is attributed to the differences in average distances involved in distance estimation for queries and documents, leading to ineffective neighbor identification.
The evaluation demonstrates that utilizing the ANN index results in a significant drop in retrieval quality compared to brute-force search, impacting recall metrics.
After incorporating the ANN index, the recall@100 metric experiences over a 10-point drop across all baseline models, indicating a critical loss in performance.
The ranking trend of models is altered when using the ANN index, with SimANS showing poorer performance than ANCE in recall@20 and recall@100.
Analysis shows that SimANS has a larger gap in distance estimates compared to ANCE, leading to inaccuracies in determining the closest documents.
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