2 citations · 2 across the 8 of their papers we have counts for
8 papers
Budget-Constrained Online Retrieval-Augmented Generation: The Chunk-as-a-Service Model
Shawqi Al-Maliki, Ammar Gharaibeh, Mohamed Rahouti +4
Large Language Models (LLMs) have revolutionized the field of natural language processing. However, they exhibit some limitations, including a lack of reliability and transparency:…
Dynamic Temperature Scheduler for Knowledge Distillation
Sibgat Ul Islam, Jawad Ibn Ahad, Fuad Rahman +3
Knowledge Distillation (KD) trains a smaller student model using a large, pre-trained teacher model, with temperature as a key hyperparameter controlling the softness of output pro…
LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval
Muhammad Rafsan Kabir, Rafeed Mohammad Sultan, Fuad Rahman +4
Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains…
BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization
Md. Nazmus Sadat Samin, Jawad Ibn Ahad, Tanjila Ahmed Medha +4
This study focuses on recognizing Bangladeshi dialects and converting diverse Bengali accents into standardized formal Bengali speech. Dialects, often referred to as regional langu…
Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis
Jawad Ibn Ahad, Rafeed Mohammad Sultan, Abraham Kaikobad +4
This study investigates the automation of meta-analysis in scientific documents using large language models (LLMs). Meta-analysis is a robust statistical method that synthesizes th…
Beyond Labels: Aligning Large Language Models with Human-like Reasoning
Muhammad Rafsan Kabir, Rafeed Mohammad Sultan, Ihsanul Haque Asif +5
Aligning large language models (LLMs) with a human reasoning approach ensures that LLMs produce morally correct and human-like decisions. Ethical concerns are raised because curren…
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
Stepan Tytarenko, Mohammad Ruhul Amin
Fine-tuning large pre-trained language models (LLMs) on particular datasets is a commonly employed strategy in Natural Language Processing (NLP) classification tasks. However, this…
BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer
Sadia Afrin, Md. Shahad Mahmud Chowdhury, Md. Ekramul Islam +10
Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. H…