Scaling Model Size and Syntax Error Reduction in CoT-Generated Code for BigCodeBench

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Does model size scaling (e.g., 7B vs. 13B vs. 30B parameters) correlate with syntax error reduction in CoT-generated code for structured data tasks on BigCodeBench. Large language models (LLMs) have demonstrated impressive performance in code generation, particularly when augmented with chain-of-thought (CoT) prompting techniques. They break down requirements into intermediate reasoning steps, which act as design rationales to guide LLMs in. 6 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Does model size scaling (e.g., 7B vs. 13B vs. 30B parameters) correlate with syntax error reduction in CoT-generated code for structured data tasks on BigCodeBench?

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