4 papers
TabDPT-Turbo: Efficient In-Context Learning for Tabular Prediction
Rasa Hosseinzadeh, Alex Labach, Zexin Xue +3
Tabular foundation models, driven by in-context learning, have rapidly grown in quality and popularity. However, recent approaches with either cell-based architectures or retrieval…
TabDPT: Scaling Tabular Foundation Models on Real Data
Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh +7
Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains. This inherent heterogeneity has slowed the development of Tabular F…
Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
Kin Kwan Leung, Mouloud Belbahri, Yi Sui +4
Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the c…
Generalization Can Emerge in Tabular Foundation Models From a Single Table
Junwei Ma, Nour Shaheen, Alex Labach +4
Deep tabular modelling increasingly relies on in-context learning where, during inference, a model receives a set of pairs as context and predicts labels for new inputs wit…