11 citations · 12 across the 5 of their papers we have counts for
7 papers
How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions
Donghao Huang, Tomas Drietomsky, Benjamin Barrett +1
Merchant information extraction turns noisy financial transaction descriptors into structured fields at production scale. Our deployed LoRA-fine-tuned LLaMA~3.1-8B reaches 96.95\%…
Beyond Task Success: Measuring Workflow Fidelity in LLM-Based Agentic Payment Systems
Donghao Huang, Joon Kiat Chua, Zhaoxia Wang
LLM-based multi-agent systems are increasingly deployed for payment workflows, yet prevailing metrics, Task Success Rate (TSR) and Agent Handoff F1-Score (HF1), capture only final…
A Novel Hierarchical Multi-Agent System for Payments Using LLMs
Joon Kiat Chua, Donghao Huang, Zhaoxia Wang
Large language model (LLM) agents, such as OpenAI's Operator and Claude's Computer Use, can automate workflows but unable to handle payment tasks. Existing agentic solutions have g…
Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis
Donghao Huang, Zhaoxia Wang
Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this clai…
Explainable Sentiment Analysis with DeepSeek-R1: Performance, Efficiency, and Few-Shot Learning
Donghao Huang, Zhaoxia Wang
Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first…
When Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems
Donghao Huang, Gauri Malwe, Zhaoxia Wang
Multi-agent systems powered by large language models (LLMs) are transforming enterprise automation, yet systematic evaluation methodologies for assessing tool-use reliability remai…