I hate this kind of ephemeral research that will be outdated in a few months. Instead of analyzing "why" at a fundamental level LLMs can or cannot do something, researchers just show what current models are or are not capable of.
And often times the results are not replicable either.
The potential utility in the research is not so much the results but the benchmark itself.
I haven’t read the whole thing so I can’t really judge whether this specific benchmark is useful, but if it is, every time a new model comes out they can run the benchmark and breathlessly report its improved performance.
> However, existing code generation benchmarks primarily focus on general-purpose scenarios, leaving the code generation performance of LLMs for specific application domains largely unknown. In this paper, we introduce a new benchmark, MultiCodeBench, to fill this gap.
This is about the benchmark they're introducing, which would have real uses for all subsequent models.
And often times the results are not replicable either.