12 citations · 19 across the 7 of their papers we have counts for
3 papers · 1 filter
MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Artem Chervyakov, Alexander Kharitonov, Pavel Zadorozhny +20
Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most b…
SWE-MERA: A Dynamic Benchmark for Agenticly Evaluating Large Language Models on Software Engineering Tasks
Pavel Adamenko, Mikhail Ivanov, Aidar Valeev +6
The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench datas…
YABLoCo: Yet Another Benchmark for Long Context Code Generation
Aidar Valeev, Roman Garaev, Vadim Lomshakov +3
Large Language Models demonstrate the ability to solve various programming tasks, including code generation. Typically, the performance of LLMs is measured on benchmarks with small…