PAMI-GPT: Knowledge-Grounded LLMs for Reliable Pattern Mining Workflow Generation

Large language models (LLMs) face significant limitations in specialized algorithmic domains because they do not reliably follow formal specifications and often produce non-executable or semantically incorrect solutions. This limitation is particularly critical in the field of pattern mining, where consistent adherence to algorithm definitions, parameter semantics, dataset formats, and library-specific interfaces is essential for producing valid results. To address this challenge, we present PAttern MIning-Generative Pre-trained Transformer (PAMI-GPT), a computational framework that integrates conversational AI with pattern mining expertise through a curated Domain Knowledge Base derived from the PAMI library and its verified algorithmic specifications. The proposed framework combines natural language understanding with domain-specific algorithm guidance and executable workflow templates at inference time. Compared to standard LLM prompting across evaluated models, PAMI-GPT achieves markedly higher semantic correctness (+ 57.0 pp), executability (+ 75.5 pp), and reduction in hallucinated APIs (− 50.4 pp). We further perform component-level ablation and inter-annotator agreement analysis to evaluate the contribution of structured retrieval and instruction-guided generation. Experimental evaluation on 300 realistic pattern mining tasks demonstrates consistent improvements in algorithm configuration accuracy, parameter compliance, and dataset specification correctness across zero-shot, one-shot, and few-shot settings. These results highlight the effectiveness of inference-time domain grounding for enabling more reliable and reproducible pattern mining workflows without requiring model retraining.