Model Size and HumanEval Score Stability Across Evaluation Protocols

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the correlation between model size (1B–175B parameters) and HumanEval score stability across different evaluation protocols (e.g., deterministic vs. probabilistic sampling). Large language models (LLMs) achieve strong performance across many natural language processing tasks, yet their decision processes remain difficult to interpret. This lack of transparency creates challenges for trust, debugging, and deployment in real-world systems. 7 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the correlation between model size (1B–175B parameters) and HumanEval score stability across different evaluation protocols (e.g., deterministic vs. probabilistic sampling)?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance