Article citationsMore>>

Latif, E., Fang, L., Ma, P. and Zhai, X. (2024) Knowledge Distillation of LLMs for Automatic Scoring of Science Assessments. In: Olney, A.M., Chounta, I.A., Liu, Z., Santos, O.C. and Bittencourt, I.I., Eds., Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024, Springer, 166-174.
https://doi.org/10.1007/978-3-031-64312-5_20

has been cited by the following article:

• TITLE: Resource Asymmetry in Multilingual NLP: A Comprehensive Review and Critique

  AUTHORS: Doyin Akindotuni

  KEYWORDS: Multilingual NLP, Resource Asymmetry, Linguistic Justice, Low-Resource Languages, Cross-Lingual Transfer, Computational Sociolinguistics

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.7, July 4, 2025

  ABSTRACT: Natural Language Processing (NLP) has witnessed significant advancements, particularly with the development of multilingual models. However, a persistent challenge remains: the resource asymmetry among languages. This interdisciplinary analysis bridges the gap between computational linguistics, sociolinguistics, and ethics to diagnose resource asymmetry in multilingual NLP. We introduce metrics (RPI, LCS, TECS) quantifying disparities and demonstrate, through case studies, how policy and community-driven models can foster equitable language. This paper provides a comprehensive analysis of the current state of resource asymmetry in multilingual NLP, examining the contributing factors, evaluating existing approaches, and identifying research gaps. Also, ethical frameworks and community-driven strategies are proposed to bridge the gaps in NLP, emphasizing participatory design and equitable resource allocation.