OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.
Tackling prediction tasks in relational databases with LLMs.arXiv preprint arXiv:2411.11829, 2024
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RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.
citing papers explorer
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OpenRFM: Dissecting Relational In-Context Learning
OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.
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RelAgent: LLM Agents as Data Scientists for Relational Learning
RelAgent uses an LLM agent to autonomously generate SQL feature programs paired with classical models for interpretable relational learning predictions that execute efficiently on standard databases.