HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads
Decoding-free reranking methods that read relevance signals directly from LLM attention weights offer significant latency advantages over autoregressive approaches, yet suffer from attention score homogenization: middle-context documents receive near-identical scores, destroying the fine-grained distinctions required for ranking. We propose HeadRank, a framework that lifts preference optimization from discrete token space into the continuous attention domain through entropy-regularized head selection, hard adjacent-level preference pairs, and a distribution regularizer that jointly sharpen discriminability in the homogenized middle zone. Depth truncation at the deepest selected layer furt...
| Authors | Juyuan Wang, Chenxing Wang, Yuchen Fang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 19 April 2026 |
| Last updated at source | 19 May 2026 |
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