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arXiv AI By Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu, Farbod Tavakkoli, Prasad Tadepalli, Kristian Kersting, Sriraam Natarajan

Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

Read the original on arXiv AI →

The paper introduces grasp, a neurosymbolic framework that learns probabilistic logic programs by combining functional gradient boosting with large language models (LLMs). In grasp, the weak learner is a first‑order rule and the combinatorial search for new rules is delegated to an LLM proposal oracle, allowing systematic inductive reasoning. Experiments on four relational benchmarks—including molecular toxicity prediction, mutagenesis, and citation matching—show that grasp outperforms purely symbolic, neural, and LLM‑based baselines while producing interpretable weighted rule ensembles.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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