Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration
Generative listwise reranking leverages global context for superior retrieval but is plagued by intrinsic position bias, where models exhibit structural sensitivity to input order independent of relevance. Existing mitigations present a dilemma: inference-time aggregation incurs prohibitive latency, while training-based methods often fail to eradicate ingrained priors, particularly in compact models. To resolve this dilemma, we propose CapCal (Content-Agnostic Probability Calibration), a training-free framework that mechanically decouples positional bias from ranking decisions. By estimating the bias distribution via content-free placeholders, CapCal rectifies output logits through an ent...
| Authors | Hang Lv, Hongchao Gu, Ruiqing Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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