LAraBench: Benchmarking Arabic AI with Large Language Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AZUFCDKCX" target="_blank" >RIV/00216208:11320/25:ZUFCDKCX - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188739126&partnerID=40&md5=5670e9e1c0622bc10e6400d628aa4093" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188739126&partnerID=40&md5=5670e9e1c0622bc10e6400d628aa4093</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

LAraBench: Benchmarking Arabic AI with Large Language Models

Original language description

Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific benchmarking against state-of-the-art (SOTA) models tailored to particular languages and tasks. LAraBench addresses this gap for Arabic Natural Language Processing (NLP) and Speech Processing tasks, including sequence tagging and content classification across different domains. We utilized models such as GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13bchat, Whisper, and USM, employing zero and few-shot learning techniques to tackle 33 distinct tasks across 61 publicly available datasets. This involved 98 experimental setups, encompassing ∼296K data points, ∼46 hours of speech, and 30 sentences for Text-to-Speech (TTS). This effort resulted in 330+ sets of experiments. Our analysis focused on measuring the performance gap between SOTA models and LLMs. The overarching trend observed was that SOTA models generally outperformed LLMs in zero-shot learning, with a few exceptions. Notably, larger computational models with few-shot learning techniques managed to reduce these performance gaps. Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks. © 2024 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

EACL - Conf. European Chapter Assoc. Comput. Linguist., Proc. Conf.

ISBN

979-889176088-2

ISSN

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e-ISSN

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Number of pages

34

Pages from-to

487-520

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

St. Julian's

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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