Relevance Scores Calibration for Ranked List Truncation via TMP Adapter

ACL ARR 2025 February Submission3756 Authors

Abstract:

The ranked list truncation task involves determining a truncation point to retrieve the relevant items from a ranked list.
Despite current advancements, truncation methods struggle with limited capacity, unstable training and inconsistency of selected threshold.
To address these problems we introduce TMP Adapter, a novel approach that builds upon the improved adapter model and incorporates the Threshold Margin Penalty (TMP) as an additive loss function to calibrate ranking model relevance scores for ranked list truncation.
We evaluate TMP Adapter’s performance on various retrieval datasets and observe that TMP Adapter is a promising advancement in the calibration methods, which offers both theoretical and practical benefits for ranked list truncation.

Paper Type: Short

Research Area: Information Retrieval and Text Mining

Research Area Keywords: passage retrieval; dense retrieval; document representation; contrastive learning

Contribution Types: NLP engineering experiment, Reproduction study

Languages Studied: English

Submission Number: 3756