Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA

• URL: http://arxiv.org/abs/2606.03728v1
• Date: Tue, 02 Jun 2026 14:48:33 GMT
• Title: Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA
• Abstract summary: Cross-encoder trained on continuous perturbation-based attribution scores to re-rank passages prior to generation.<n>Two re-rankers trained independently on different models converge beyond their raw attribution agreement.<n>Results demonstrate that perturbation-based attribution provides a practical, model-agnostic training signal for citation-aware retrieval.
• Score: 2.255961793913651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation systems for legal question answering typically retrieve passages based on semantic similarity and provide them to a language model, which then generates cited answers. Prior work assumes that highly ranked passages are most likely to be usefully cited by the model. Perturbation-based attribution methods, such as C-LIME, have been used exclusively for post-hoc explanation. However, on the AQuAECHR benchmark, semantic similarity does not correlate with passage attribution. Within a retriever's candidate pool, similarity-based ranking performs worse than random selection at surfacing gold citation paragraphs. To address this limitation, a lightweight cross-encoder is trained on continuous perturbation-based attribution scores to re-rank passages prior to generation. This approach is evaluated on the AQuAECHR benchmark, using two language models and five-fold cross-validation. The re-ranker substantially improves citation faithfulness and alignment with gold expert answers. Notably, two re-rankers trained independently on different models converge beyond their raw attribution agreement. This finding indicates that the cross-encoder reduces model-specific noise and produces a shared relevance signal that partially transfers across models, although same-model re-ranking remains more effective. These results demonstrate that perturbation-based attribution provides a practical, model-agnostic training signal for citation-aware retrieval.

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