MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
This addresses a gap in evaluating code generation LLMs for practical, non-English tasks, though it is incremental as it extends existing benchmark frameworks to a specific domain.
The authors tackled the lack of code quality evaluation for LLMs in software engineering by proposing MERA Code, a benchmark for assessing code generation in Russian across 8 programming languages and 11 tasks, evaluating open and frontier models to identify limitations in non-English contexts.
Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most benchmarks prioritize high-level reasoning over executable code and real-world performance, leaving gaps in understanding true capabilities and risks associated with these models in production. To address this issue, we propose MERA Code, a new addition to the MERA benchmark family, specifically focused on evaluating code for the latest code generation LLMs in Russian. This benchmark includes 11 evaluation tasks that span 8 programming languages. Our proposed evaluation methodology features a taxonomy that outlines the practical coding skills necessary for models to complete these tasks. The benchmark comprises an open-source codebase for users to conduct MERA assessments, a scoring system compatible with various programming environments, and a platform featuring a leaderboard and submission system. We evaluate open LLMs and frontier API models, analyzing their limitations in terms of practical coding tasks in non-English languages. We are publicly releasing MERA to guide future research, anticipate groundbreaking features in model development, and standardize evaluation procedures.