Work

Bridging the AI Divide: Building Explainable, Low-Resource Multilingual LLMs for Inclusive Global Access

Year: 2025

Type: article

Abstract: This paper presents LegiLLM, a multilingual and explainable large language model framework designed for low-resource Indian languages such as Kannada, Odia, and Assamese. Leveraging fine-tuned transformer models (IndicBERT and XLM-R), we explore explainable AI techniques like LIME and attention heatmaps to provide interpretable reasoning in low-compute environments. Our work aims to bridge the AI accessibility gap for underrepresented communities and support democratized, ethical machine learning across diverse linguistic groups.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15777254

Author: Dutta, Dikshita

Institution: Jain University

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: green

OpenAlex ID: W6931028709

FWCI:

Cites:

Cited by:

Related to: