ALBI: LLM-Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refinement
ALBI is a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures paradigm with an LLM, while using SWI-Prolog as a formal verifier. It implements an iterative generate-test-constrain loop where the LLM proposes hypotheses, Prolog tests them, and diagnostic feedback drives refinement. On 30 standard ILP benchmarks, ALBI achieves 100% success across three frontier LLMs, matching Popper with only 1.4-1.7 iterations on average. Semantic anonymization tests confirm robust symbolic reasoning, with all models maintaining 100% success despite a moderate increase in iterations.
Why it matters: This paper presents a novel integration of LLMs with formal verification for ILP, demonstrating high success and robustness, which is valuable for researchers and practitioners in neuro-symbolic AI.