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attention calibration

topic1 events
papersTODAY 04:00 UTC

arXiv paper proposes attention calibration for position-fair dense retrieval

A revised arXiv paper addresses a known weakness in dense retrieval: compressing a passage into a single embedding tends to weight early text more heavily, so retrieval quality drops when the relevant span sits later in the passage. The authors propose attention calibration as a way to reduce this positional bias, building on earlier inference-time approaches. It is a research contribution rather than a released product.

dense-retrieval/arxiv/nlp/embeddings