160 citations · 192 across the 7 of their papers we have counts for
8 papers
Continuous Process-Level Evaluation for Evolving Enterprise AI Agent Skills
Ngoc Phuoc An Vo, Aarya Doshi, Vadim Sheinin
Enterprise AI agent skills evolve as tool APIs, models, and specifications change, yet final-output evaluation can miss process-level behavioral drift. We present a continuous eval…
LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin
In an effort to automatically evaluate and select the best model and improve code quality for automatic incident remediation in IT Automation, it is crucial to verify if the genera…
Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident Remediation
Ngoc Phuoc An Vo, Brent Paulovicks, Vadim Sheinin
Given recent advancements of Large Language Models (LLMs), code generation tasks attract immense attention for wide application in different domains. In an effort to evaluate and s…
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found
Irene Manotas, Octavian Popescu, Ngoc Phuoc An Vo +1
There are many recent advanced developments for the Text-to-SQL task, where the Picard model is one of the the top performing models as measured by the Spider dataset competition.…
Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management
Ngoc Phuoc An Vo, Irene Manotas, Octavian Popescu +2
Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One…
SQL-to-Text Generation with Graph-to-Sequence Model
Kun Xu, Lingfei Wu, Zhiguo Wang +2
Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this p…
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Kun Xu, Lingfei Wu, Zhiguo Wang +3
Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information su…
Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks
Kun Xu, Lingfei Wu, Zhiguo Wang +3
The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have in…