Summary of Arablegaleval: a Multitask Benchmark For Assessing Arabic Legal Knowledge in Large Language Models, by Faris Hijazi (1) et al.
ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models
by Faris Hijazi, Somayah AlHarbi, Abdulaziz AlHussein, Harethah Abu Shairah, Reem AlZahrani, Hebah AlShamlan, Omar Knio, George Turkiyyah
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 abstract discusses the development of a new benchmark dataset called ArabLegalEval, designed to evaluate the legal knowledge of Large Language Models (LLMs) in Arabic. The dataset consists of multiple tasks inspired by existing datasets like MMLU and LegalBench, and is sourced from Saudi legal documents with synthesized questions. The authors aim to analyze the capabilities required for solving legal problems in Arabic and benchmark the performance of state-of-the-art LLMs, including GPT-4 and Jais. They also explore the impact of in-context learning and various evaluation methods. Furthermore, they share their methodology for creating the dataset and validation, which can be generalized to other domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ArabLegalEval is a new dataset that tests how well language models understand Arabic legal documents. Right now, there aren’t many datasets like this that focus on Arabic or other non-English languages. The authors created ArabLegalEval by using Saudi legal documents and making questions about them. They want to see what language models can do with these tasks and which ones are best at understanding Arabic legal texts. |
Keywords
» Artificial intelligence » Gpt
