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

Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA arXiv:2606.03728v1 Announce Type: new 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