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In practical vector retrieval systems, a globally fixed similarity threshold is usually set to filter out low-quality results, but the threshold makes it hard to adapt to the semantic needs of different queries.
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10/28/2026
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UnverifiedThe absolute value of a retrieval score threshold is highly dependent on specific conditions such as the type of embedding model, document length and chunking granularity, and how the query is phrased, and it cannot be assumed to be universally valid.76% similarUnverifiedVector distance and retrieval thresholds are a trade-off between recall and accuracy: the looser the threshold (the larger the value), the more is recalled, but the higher the probability of irrelevant content being mixed in; tightening the threshold may miss answers that could have been reused.75% similarUnverifiedQuestions with explicit constraints (time, category, permissions) should be resolved first with filtering, while semantically ambiguous questions are handled by vector search—the two complement rather than replace each other.72% similarUnverifiedLEANN's limitations include: second-level retrieval is not suited to the stringent latency requirements of interactive question answering; recomputing embeddings depends on the availability of the original data; and the recall performance of the pruned graph needs to be validated for different data distributions.72% similarUnverifiedA similarity threshold that is too high may cause valid answers to be incorrectly filtered out, while one that is too low may introduce noise, requiring a trade-off between recall and precision.71% similar
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