4 papers
RA-ClipScore: Making Generative Model Evaluation More Interpretable
Yifan Lu, Taras Kucherenko, Hedvig Kjellström +1
Generative models can produce images nearly indistinguishable from real data, yet rigorous and interpretable evaluation remains challenging. Conventional metrics such as FID provid…
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
Zineb Senane, Axel Karlsson, Lele Cao +6
Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pa…
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas, Xavier Sumba, Tanmayee Narendra +4
Causal discovery, i.e., inferring underlying causal relationships from observational data, is highly challenging for AI systems. In a time series modeling context, traditional caus…
CARL-GT: Evaluating Causal Reasoning Capabilities of Large Language Models
Ruibo Tu, Hedvig Kjellström, Gustav Eje Henter +1
Causal reasoning capabilities are essential for large language models (LLMs) in a wide range of applications, such as education and healthcare. But there is still a lack of benchma…