Where Do LLMs Still Struggle? An In-Depth Analysis of Code Generation Benchmarks
This work addresses a gap in understanding LLM limitations in code generation, providing insights for researchers and developers to improve model capabilities, though it is incremental as it builds on existing benchmarks.
The paper analyzed code generation tasks across four benchmarks to identify where large language models (LLMs) consistently fail, revealing four recurring patterns of weaknesses and common complications in tasks that lead to failure.
Large Language Models (LLMs) have achieved remarkable success in code generation, and the race to improve their performance has become a central focus of AI research. Benchmarks and leaderboards are increasingly popular, offering quantitative rankings of LLMs. However, they provide limited insight into the tasks that LLMs consistently fail to solve - information that is crucial for understanding current limitations and guiding the development of more capable models. To address this gap, we examined code generation tasks across four popular benchmarks, identifying those that major LLMs are most likely to fail. To understand the causes of these failures, we investigated whether the static complexity of solution code contributes to them, followed by a systematic inspection of 114 tasks that LLMs consistently struggled with. Our analysis revealed four recurring patterns of weaknesses in LLMs, as well as common complications within benchmark tasks that most often lead to failure.