Query-focused and Memory-aware Reranker for Long Context Processing
This addresses the problem of efficient and accurate passage reranking for long-context tasks like dialogue understanding, though it appears incremental as it builds on existing analysis of retrieval heads.
The authors tackled the problem of reranking passages for query-focused long context processing by proposing a lightweight framework that uses attention scores from selected heads in language models to estimate relevance. Their method outperformed existing state-of-the-art rerankers across multiple domains, establishing a new SOTA on the LoCoMo benchmark.
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.