most citedComprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering

3 citations · 3 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL2025

mSCoRe: a ultilingual and Scalable Benchmark for kill-based mmonsense asoning

Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen

Recent advancements in reasoning-reinforced Large Language Models (LLMs) have shown remarkable capabilities in complex reasoning tasks. However, the mechanism underlying their util…

cs.CV2025

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

Mohamed Elmoghany, Ryan Rossi, Seunghyun Yoon +26

Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-…

cs.LG2025

From Selection to Generation: A Survey of LLM-based Active Learning

Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie +31

Active Learning (AL) has been a powerful paradigm for improving model efficiency and performance by selecting the most informative data points for labeling and training. In recent…

cs.CL2025

LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models

Hieu Man, Nghia Trung Ngo, Viet Dac Lai +3

Recent advancements in large language models (LLMs) based embedding models have established new state-of-the-art benchmarks for text embedding tasks, particularly in dense vector-b…

cs.AI2024

GUI Agents: A Survey

Dang Nguyen, Jian Chen, Yu Wang +27

Graphical User Interface (GUI) agents, powered by Large Foundation Models, have emerged as a transformative approach to automating human-computer interaction. These agents autonomo…

cs.CL20243 cited

Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering

Nghia Trung Ngo, Chien Van Nguyen, Franck Dernoncourt +1

Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from…