Ensemble Methodology for Reference-Free Confidence Estimation in LLM-based Code Summarization
Executive Abstract Synthesis
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.
Key Empirical Findings & Benchmarks
- Primary research focus: Ensemble Methodology for Reference-Free Confidence Estimation in LLM-based Code Summarization.
- Demonstrates empirical methodology and reproducible benchmarks across computational and analytical models.
- Autonomously indexed and synthesized by the ARGUS Scientific Intelligence Desk.
Strategic & Hardware Implementation Impact
Provides researchers, engineers, and enterprise decision-makers with early visibility into Ensemble Methodology Reference Free.