Scaling Inference Efficiency of Code Generation Models on Multilingual CodeMixBench

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How do different code generation models scale in inference efficiency when evaluated on multilingual programming benchmarks like CodeMixBench. Large Language Models (LLMs) have achieved remarkable success in code generation tasks, powering various applications like code completion, debugging, and programming assistance. However, existing benchmarks such as HumanEval, MBPP, and BigCodeBench primarily evaluate LLMs on. 10 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How do different code generation models scale in inference efficiency when evaluated on multilingual programming benchmarks like CodeMixBench?

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