Cassette: Case-to-Case Structural Distillation for Efficient Legal Case Retrieval

Legal case retrieval (LCR) is an essential tool for not only assisting legal practitioners to efficiently retrieve precedents, but also enabling ordinary individuals to find valuable legal case information without relying on expensive professional legal service. Our previous work CaseLink [51] demonstrated the effectiveness of using case to case graph structures to improve retrieval accuracy. However, its high computational cost during inference on large scale legal databases limits its practical use in real world settings. The main inefficiency comes from constructing test time graphs and computing pairwise term frequency similarities of cases. This process has \(O(n^{2})\) complexity for \(n\) legal cases, making the runtime prohibitive as the number of candidates grows. For example, the retrieval time for one query on a database (COLIEE2022 [21]) with 1,563 candidate cases is more than 500 milliseconds , while the runtime would increase drastically to more than 3,500 seconds for a database (LeCaRDv2 [25]) with 55,192 candidate cases . To further enhance the retrieval performance while achieving a significant speed-up, in this extension paper, Cassette framework is proposed with a distillation strategy involving ranking objective and eigen-matching objective for an effective transfer of knowledge from a powerful and well-trained heavy teacher retriever to a lightweight and efficient hybrid student dual encoder. Specifically, the student query encoder is implemented as a multilayer perceptron model designed for fast online processing, whereas the student candidate encoder adopts a GNN architecture, suitable for an offline manner within the case database. Extensive experiments are conducted on three benchmark datasets and the results verify the effectiveness of the ranking distillation while achieving high efficiency. The code has been released on https://github.com/yanran-tang/Cassette/.