CERA: Context-Engineered Reviews Architecture for LLM-based Synthetic Dataset Generation
Kap Thang, Danial Ebrat, Luis Rueda
CanadianAI '26 - Proceedings of the 39th Canadian Conference on Artificial Intelligence
Proposed a training-free three-phase framework (Composition, Generation, Evaluation) for generating realistic synthetic review datasets for Aspect-Based Sentiment Analysis using LLMs, achieving real-data-level corpus diversity and near-chance-level human detection rates.
LLMsSynthetic DataAspect-Based Sentiment AnalysisContext EngineeringNLP
OpeNTF2: Fairness-aware Graph Neural Team Formation
Hamed Loghmani, Md Jamil Ahmed, Kap Thang, Gabriel Rueda, Hossein Fani
SIGIR '26 - Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval
A unified framework for neural team formation integrating fairness-aware debiasing rerankers, graph-based modeling via GNNs, sequence prediction with transformers, and temporal training strategies across four large-scale datasets.
Fair Team FormationSocial Information RetrievalGraph Neural NetworksOpeNTF
Translative Neural Team Recommendation: From Multilabel Classification to Sequence Prediction
Kap Thang, Hawre Hosseini, Hossein Fani
SIGIR '25 - Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval
We reformulated team recommendation as a sequence-to-sequence task using transformer architectures, achieving up to 82x improvement over existing feedforward neural approaches. Our method was evaluated on 4 large-scale datasets (DBLP, USPTO, IMDB, GitHub) with distinct skill/expert distributions, consistently outperforming baselines across all metrics.
Team RecommendationTransformersSequence-to-SequenceNeural Networks