Summary of Gpts and Language Barrier: a Cross-lingual Legal Qa Examination, by Ha-thanh Nguyen et al.
GPTs and Language Barrier: A Cross-Lingual Legal QA Examination
by Ha-Thanh Nguyen, Hiroaki Yamada, Ken Satoh
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the use of Generative Pre-trained Transformers (GPTs) for cross-lingual legal Question-Answering (QA) systems. Specifically, it applies GPTs to the COLIEE Task 4 dataset, which involves determining whether a given statement is legally valid based on provided contextual articles. The study benchmarks four combinations of English and Japanese prompts and data, providing insights into GPTs’ performance in multilingual legal QA scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses Generative Pre-trained Transformers (GPTs) to help computers answer questions about laws in different languages. It tries out different ways of asking the same question in both English and Japanese to see how well the computer does. This helps us make computers that can understand laws from different countries, which is important for international business and law. |
Keywords
» Artificial intelligence » Question answering
