5 papers
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…
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam, Munyeong Kim, Frida-Cecilia Acosta-Parenteau +4
How do we update AI memory of user intent as intent changes? We consider how an AI interface may assist the integration of new information into a repository of natural language dat…
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB
Anas Dorbani, Sunny Yasser, Jimmy Lin +1
Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective decision-making. Large language m…
GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise
Karime Maamari, Connor Landy, Amine Mhedhbi
Recent advancements in Text-to-SQL, driven by large language models, are democratizing data access. Despite these advancements, enterprise deployments remain challenging due to the…
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh +1
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve s…