2 citations · 4 across the 5 of their papers we have counts for
5 papers
Compiling Large Multi-Modal Requirement Documents into Runnable Software Systems: From an Agentic Test-Driven Perspective
Weiyu Kong, Yun Lin, Xiwen Teoh +5
Large Language Models (LLMs) have significantly improved programming efficiency by translating natural language into code, yet their performance deteriorates when handling large-sc…
WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements
Xiwen Teoh, Yun Lin, Duc-Minh Nguyen +3
Visual language model (VLM) agents show great promise in automating end-to-end (E2E) web testing against requirements in natural language. However, the probabilistic nature of lang…
Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval
Hai-Long Nguyen, Tan-Minh Nguyen, Duc-Minh Nguyen +3
Statutory law retrieval is a typical problem in legal language processing, that has various practical applications in law engineering. Modern deep learning-based retrieval methods…
DG Comics: Semi-Automatically Authoring Graph Comics for Dynamic Graphs
Joohee Kim, Hyunwook Lee, Duc M. Nguyen +4
Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs -- graphs whose vertices and edges change over time. However, manually creatin…
Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models
Hai-Long Nguyen, Duc-Minh Nguyen, Tan-Minh Nguyen +3
Large language models with billions of parameters, such as GPT-3.5, GPT-4, and LLaMA, are increasingly prevalent. Numerous studies have explored effective prompting techniques to h…