Selecting Context for Rule-Grounded Explanation Generation: A Small Language Model Study in Injection Moulding Quality Prediction

Abstract

Tree ensembles predict product quality accurately from process-condition data but are opaque, and rule extraction addresses this by compressing an ensemble into a small set of decision rules. A ruleset, however, is a technical representation, and the operators, production managers, and data scientists who act on a quality prediction each require something different from the same ruleset. Small language models deployed on local hardware can express a rule in the terms each audience requires, but they carry little process-specific knowledge and must therefore be grounded in context supplied at inference time. Systems of this kind often supply all available context by default, but the value of this has not been tested. This study presents RuleSLM, which grounds a compact language model in a ruleset extracted from an XGBoost model trained to detect sink mark defects in injection moulding, and decomposes the supplied context into rule evidence, knowledge, and interaction guidance. Crossing the latter two in a factorial design over four small language models and 58 role- and intent-annotated questions shows that interaction guidance increases the evaluator’s role-appropriateness score by 0.323 points on a five-point rubric, that knowledge context changes no dimension measurably, and that the two components interact in opposite directions on different models. Supplying the full context increases the input length by 275% and energy per response by 50% for a benefit confined to one of three quality dimensions. Repeated evaluation further shows that the rubric-based evaluator reproduces fewer than half of the individual scores while producing configuration-by-dimension means that are stable to within 0.12 points. Context for rule-grounded explanation should therefore be selected for the property it serves and validated on the model that will be deployed.