6 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Dissecting Neuro-Symbolic Quality Assurance for Synthetic Oncology Data Generation
Laxmigayathri Challa, Yuhan Zhou, Ana Cleveland +1
Synthetic clinical data generation with large language models addresses the scarcity that limits cancer staging research, but oncology hallucinations are categorically harmful: one…
MetaGAI: A Large-Scale and High-Quality Benchmark for Generative AI Model and Data Card Generation
Haoxuan Zhang, Ruochi Li, Yang Zhang +4
The rapid proliferation of Generative AI necessitates rigorous documentation standards for transparency and governance. However, manual creation of Model and Data Cards is not scal…
LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation
Huyen Nguyen, Haoxuan Zhang, Yang Zhang +2
Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document lengths. We conduct a compre…
LongSumEval: Question-Answering Based Evaluation and Feedback-Driven Refinement for Long Document Summarization
Huyen Nguyen, Haoxuan Zhang, Yang Zhang +2
Evaluating long document summaries remains the primary bottleneck in summarization research. Existing metrics correlate weakly with human judgments and produce aggregate scores wit…
RoTRAG: Rule of Thumb Reasoning for Conversation Harm Detection with Retrieval-Augmented Generation
Juhyeon Lee, Wonduk Seo, Junseo Koh +3
Detecting harmful content in multi turn dialogue requires reasoning over the full conversational context rather than isolated utterances. However, most existing methods rely mainly…
AdaQE-CG: Adaptive Query Expansion for Web-Scale Generative AI Model and Data Card Generation
Haoxuan Zhang, Ruochi Li, Zhenni Liang +7
Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for generating model and data card…