Mistral-Large-2 Code Generation on MBPP: Correctness and Quality vs. Human Baselines

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How do Mistral-Large-2 generated code solutions on MBPP compare to ground truth implementations in terms of functional correctness and code quality as measured by human evaluation scores. In recent years, researchers have proposed numerous benchmarks to evaluate the impressive coding capabilities of large language models (LLMs). However, current benchmarks primarily assess the accuracy of LLM-generated code, while neglecting other critical dimensions that also. 11 claims were extracted from source literature; 2 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do Mistral-Large-2 generated code solutions on MBPP compare to ground truth implementations in terms of functional correctness and code quality as measured by human evaluation scores?

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