The Emergence of Relevance Through Axiomatic Attention Patterns During LoRA Fine-Tuning
LoRA fine-tuning is standard for adapting LLMs to reranking, but it remains unclear where in the network task-specific relevance behavior is learned and what attention-level changes accompany that learning. Through ablation and attention experiments, we identify where LoRA attention updates to RankLLaMA improve performance and whether those gains coincide with interpretable relevance-oriented attention patterns such as lexical matching, rarity sensitivity, and query-document interaction. We find that given LoRA fine-tuned MLPs throughout the network, restricting LoRA attention updates to a compact mid-network region is sufficient for recovering over half of the performance gained by apply...
| Authors | Matthew Perlman, Atharva Nijasure, James Allan |
|---|---|
| Primary category | cs.CL |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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