ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models
- URL: http://arxiv.org/abs/2408.07983v1
- Date: Thu, 15 Aug 2024 07:09:51 GMT
- Title: ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models
- Authors: Faris Hijazi, Somayah AlHarbi, Abdulaziz AlHussein, Harethah Abu Shairah, Reem AlZahrani, Hebah AlShamlan, Omar Knio, George Turkiyyah,
- Abstract summary: ArabLegalEval is a benchmark dataset for assessing the Arabic legal knowledge of Large Language Models (LLMs)
Inspired by the MMLU and LegalBench datasets, ArabLegalEval consists of multiple tasks sourced from Saudi legal documents and synthesized questions.
We aim to analyze the capabilities required to solve legal problems in Arabic and benchmark the performance of state-of-the-art LLMs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid advancements in Large Language Models (LLMs) have led to significant improvements in various natural language processing tasks. However, the evaluation of LLMs' legal knowledge, particularly in non-English languages such as Arabic, remains under-explored. To address this gap, we introduce ArabLegalEval, a multitask benchmark dataset for assessing the Arabic legal knowledge of LLMs. Inspired by the MMLU and LegalBench datasets, ArabLegalEval consists of multiple tasks sourced from Saudi legal documents and synthesized questions. In this work, we aim to analyze the capabilities required to solve legal problems in Arabic and benchmark the performance of state-of-the-art LLMs. We explore the impact of in-context learning and investigate various evaluation methods. Additionally, we explore workflows for generating questions with automatic validation to enhance the dataset's quality. We benchmark multilingual and Arabic-centric LLMs, such as GPT-4 and Jais, respectively. We also share our methodology for creating the dataset and validation, which can be generalized to other domains. We hope to accelerate AI research in the Arabic Legal domain by releasing the ArabLegalEval dataset and code: https://github.com/Thiqah/ArabLegalEval
Related papers
- A Survey of Large Language Models for Arabic Language and its Dialects [0.0]
This survey offers a comprehensive overview of Large Language Models (LLMs) designed for Arabic language and its dialects.
It covers key architectures, including encoder-only, decoder-only, and encoder-decoder models, along with the datasets used for pre-training.
The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks.
arXiv Detail & Related papers (2024-10-26T17:48:20Z) - AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs [22.121471902726892]
We present AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation.
First-ever fine-grained benchmark designed to evaluate cultural awareness across the Gulf, Egypt, and Levant regions.
We will release the dialectal translation models and benchmarks curated in this study.
arXiv Detail & Related papers (2024-09-17T17:59:25Z) - Exploring Retrieval Augmented Generation in Arabic [0.0]
Retrieval Augmented Generation (RAG) has emerged as a powerful technique in natural language processing.
This paper presents a case study on the implementation and evaluation of RAG for Arabic text.
arXiv Detail & Related papers (2024-08-14T10:03:28Z) - ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
We present datasetname, the first multi-task language understanding benchmark for the Arabic language.
Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region.
Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
arXiv Detail & Related papers (2024-02-20T09:07:41Z) - Zero-Shot Cross-Lingual Reranking with Large Language Models for
Low-Resource Languages [51.301942056881146]
We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages.
Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba)
We examine cross-lingual reranking with queries in English and passages in the African languages.
arXiv Detail & Related papers (2023-12-26T18:38:54Z) - Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction
Following: A Case Study of Arabic [1.0878040851638]
We employ GPT-4 as a uniform evaluator for both English and Arabic queries to assess and compare the performance of the LLMs on various open-ended tasks.
We find that fine-tuned base models using multilingual and multi-turn datasets could be competitive to models trained from scratch on multilingual data.
arXiv Detail & Related papers (2023-10-23T11:40:04Z) - AceGPT, Localizing Large Language Models in Arabic [73.39989503874634]
The paper proposes a comprehensive solution that includes pre-training with Arabic texts, Supervised Fine-Tuning (SFT) utilizing native Arabic instructions, and GPT-4 responses in Arabic.
The goal is to cultivate culturally cognizant and value-aligned Arabic LLMs capable of accommodating the diverse, application-specific needs of Arabic-speaking communities.
arXiv Detail & Related papers (2023-09-21T13:20:13Z) - Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
Existing large language models show disparate capability across different languages.
In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
arXiv Detail & Related papers (2023-08-09T13:32:06Z) - One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support [18.810320088441678]
This work introduces a novel NLP benchmark for the legal domain.
It challenges LLMs in five key dimensions: processing emphlong documents (up to 50K tokens), using emphdomain-specific knowledge (embodied in legal texts) and emphmultilingual understanding (covering five languages)
Our benchmark contains diverse datasets from the Swiss legal system, allowing for a comprehensive study of the underlying non-English, inherently multilingual legal system.
arXiv Detail & Related papers (2023-06-15T16:19:15Z) - Adapters for Enhanced Modeling of Multilingual Knowledge and Text [54.02078328453149]
Language models have been extended to multilingual language models (MLLMs)
Knowledge graphs contain facts in an explicit triple format, which require careful curation and are only available in a few high-resource languages.
We propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages.
arXiv Detail & Related papers (2022-10-24T21:33:42Z) - X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained
Language Models [103.75890012041366]
Language models (LMs) have proven surprisingly successful at capturing factual knowledge.
However, studies on LMs' factual representation ability have almost invariably been performed on English.
We create a benchmark of cloze-style probes for 23 typologically diverse languages.
arXiv Detail & Related papers (2020-10-13T05:29:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.