2 citations · 5 across the 10 of their papers we have counts for
12 papers
KG-Reasoner: A Reinforced Model for End-to-End Multi-Hop Knowledge Graph Reasoning
Shuai Wang, Yinan Yu
Large Language Models (LLMs) exhibit strong abilities in natural language understanding and generation, yet they struggle with knowledge-intensive reasoning. Structured Knowledge G…
KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Shuai Wang, Yinan Yu
Large Language Models (LLMs) demonstrate impressive natural language capabilities but often struggle with knowledge-intensive reasoning tasks. Knowledge Base Question Answering (KB…
Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting
Shuai Wang, Xixi Wang, Yinan Yu
Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question An…
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
Shuai Wang, Yinan Yu, Earl Barr +1
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI co…
DomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
Shuai Wang, Dhasarathy Parthasarathy, Robert Feldt +1
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-…
Latent Domain Prompt Learning for Vision-Language Models
Zhixing Li, Arsham Gholamzadeh Khoee, Yinan Yu
The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applicati…
iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
Large Language Models (LLMs) excel in many natural language processing tasks but often exhibit factual inconsistencies in knowledge-intensive settings. Integrating external knowled…
Polymer: Development Workflows as Software
Dhasarathy Parthasarathy, Yinan Yu, Earl T. Barr
Software development builds digital tools to automate processes, yet its initial phases, up to deployment, remain largely manual. There are two reasons: Development tasks are often…
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
Arsham Gholamzadeh Khoee, Shuai Wang, Robert Feldt +2
Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domai…
Automating a Complete Software Test Process Using LLMs: An Automotive Case Study
Shuai Wang, Yinan Yu, Robert Feldt +1
Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control v…
GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt +3
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to…
Domain Generalization through Meta-Learning: A Survey
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inev…