2 citations · 3 across the 11 of their papers we have counts for
12 papers
ProSEA: Problem Solving via Exploration Agents
William Nguyen, Vinh Luong, Christopher Nguyen
Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions…
KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation
Ziyi Guan, Jason Chun Lok Li, Zhijian Hou +11
Despite recent progress, Graphic User Interface (GUI) agents powered by Large Language Models (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge. While…
A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support
Long S. T. Nguyen, Truong P. Hua, Thanh M. Nguyen +6
With the rapid growth of Artificial Intelligence, Large Language Models (LLMs) have become essential for Question Answering (QA) systems, improving efficiency and reducing human wo…
Language Models for Adult Service Website Text Analysis
Nickolas Freeman, Thanh Nguyen, Gregory Bott +2
Sex trafficking refers to the use of force, fraud, or coercion to compel an individual to perform in commercial sex acts against their will. Adult service websites (ASWs) have and…
Llamarine: Open-source Maritime Industry-specific Large Language Model
William Nguyen, An Phan, Konobu Kimura +5
Large Language Models (LLMs) have demonstrated substantial potential in addressing complex reasoning tasks, yet their general-purpose nature often limits their effectiveness in spe…
SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model
Christopher Nguyen, William Nguyen, Atsushi Suzuki +10
Large Language Models (LLMs) have demonstrated the potential to address some issues within the semiconductor industry. However, they are often general-purpose models that lack the…
Learning Code Preference via Synthetic Evolution
Jiawei Liu, Thanh Nguyen, Mingyue Shang +5
Large Language Models (LLMs) have recently demonstrated remarkable coding capabilities. However, assessing code generation based on well-formed properties and aligning it with deve…
DANA: Domain-Aware Neurosymbolic Agents for Consistency and Accuracy
Vinh Luong, Sang Dinh, Shruti Raghavan +9
Large Language Models (LLMs) have shown remarkable capabilities, but their inherent probabilistic nature often leads to inconsistency and inaccuracy in complex problem-solving task…
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
Tuan-Dung Bui, Duc-Thieu Luu-Van, Thanh-Phat Nguyen +3
Code completion is essential in software development, helping developers by predicting code snippets based on context. Among completion tasks, Method Body Completion (MBC) is parti…
xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks
Duc Q. Nguyen, Thanh Toan Nguyen, Tho quan
Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science. It involves determining whether a given query…
Connector 0.5: A unified framework for graph representation learning
Thanh Sang Nguyen, Jooho Lee, Van Thuy Hoang +1
Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream task…
Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot
Trieu Hai Nguyen, Thi-Kim-Ngoan Pham, Thi-Hong-Minh Bui +1
The biggest challenge of building chatbots is training data. The required data must be realistic and large enough to train chatbots. We create a tool to get actual training data fr…