Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models
Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document relevance. However, existing methods either aggregate attention signals across all heads or rely on a statically selected subset identified by heuristic rules. This solution can be suboptimal because the informative heads can vary across queries or domains. Moreover, naively combining multiple heads can degrade performance due to redundancy or conflicting ranking signals. In this paper, we propose a query-dependent head selection method, RouteHead, for attention-based re-ranking with LLMs. Specifically, we learn a lightweight router that can ma...
| Authors | Yuxing Tian, Fengran Mo, Zhiqi Huang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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