Query-focused and Memory-aware Reranker for Long Context Processing

TL;DR

This paper introduces a lightweight, attention-based reranking framework for long context processing that outperforms existing methods and supports flexible extensions, improving retrieval accuracy across multiple domains.

Contribution

It proposes a novel listwise reranking approach leveraging attention scores, enabling effective long-context processing without extensive supervision or large models.

Findings

Outperforms state-of-the-art rerankers on multiple datasets

Achieves new SOTA on the LoCoMo benchmark

Supports flexible extensions like contextual augmentation

Abstract

Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage-query relevance using the attention scores of selected heads. This approach provides a listwise solution that leverages holistic information within the entire candidate shortlist during ranking. At the same time, it naturally produces continuous relevance scores, enabling training on arbitrary retrieval datasets without requiring Likert-scale supervision. Our framework is lightweight and effective, requiring only small-scale models (e.g., 4B parameters) to achieve strong performance. Extensive experiments demonstrate that our method outperforms existing state-of-the-art pointwise and listwise rerankers across multiple domains, including Wikipedia and long narrative datasets. It further establishes a new state-of-the-art on the LoCoMo benchmark that assesses the capabilities of dialogue understanding and memory usage. We further demonstrate that our framework supports flexible extensions. For example, augmenting candidate passages with contextual information further improves ranking accuracy, while training attention heads from middle layers enhances efficiency without sacrificing performance.