Multilingual LLM Ranking Consistency Across Code and Knowledge Benchmarks at Scale

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the ranking consistency of multilingual LLMs on technical code generation benchmarks like HumanEval-Multi compare to their performance on general knowledge benchmarks as model scale increases. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the ranking consistency of multilingual LLMs on technical code generation benchmarks like HumanEval-Multi compare to their performance on general knowledge benchmarks as model scale increases?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.