AI RESEARCH
Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA
arXiv CS.CL
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ArXi:2606.03728v1 Announce Type: new 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.