Ensemble Methodology for Reference-Free Confidence Estimation in LLM-based Code Summarization
[Sztuczna inteligencja] Autorzy: Marco Colombo, Balaji A Jayaram, Sowmyakumari Selvaraj i 5 więcej
This paper describes an approach and use case to estimate the correctness of LLM-generated COBOL summaries without access to human-authored references. The core intuition is that inter-model agreement on semantics is informative about truth, and that an LLM-as-judge can provide higher-fidelity pairwise assessments than traditional string- or embedding-based metrics. We operationalise this intuition with (i) controlled summary generation from diverse models; (ii) a LLM evaluator that compares pairs of summaries and emits structured judgments; and (iii) a calibrated aggregation rule that combines supportive evidence while penalising only corroborated contradictions.
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja