GPTs and Language Barrier: A Cross-Lingual Legal QA Examination
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Abstract (arXiv)
In this paper, we explore the application of Generative Pre-trained Transformers (GPTs) in cross-lingual legal Question-Answering (QA) systems using the COLIEE Task 4 dataset. In the COLIEE Task 4, given a statement and a set of related legal articles that serve as context, the objective is to determine whether the statement is legally valid, i.e., if it can be inferred from the provided contextual articles or not, which is also known as an entailment task. By benchmarking four different combinations of English and Japanese prompts and data, we provide valuable insights into GPTs' performance in multilingual legal QA scenarios, contributing to the development of more efficient and accurate cross-lingual QA solutions in the legal domain.
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References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2023Can GPT-3 Perform Statutory Reasoning?cited by 71
- 2021Yes, BM25 is a Strong Baseline for Legal Case Retrievalcited by 24
- 2022Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Taskcited by 8
- 2023NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processingcited by 4
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