DivAlgo: A systematic evaluation of algorithmic diversity for code generation models
Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to generate algorithmically diverse code remains largely unknown. We propose the DivAlgo benchmark, a large-scale dataset constituting algorithmically diverse solutions to programming problems. We also introduce a lightweight similarity metric to enable large-scale evaluation. Our experiments across 24 modern code generation models found that uncontrolled model-generated code is significantly less diverse than human solutions (capturing ~31% of human diversity), and even the best diverse code generation strategy could achieve only ~70% of human d...
| Authors | Anubhav Jangra, Smaranda Muresan |
|---|---|
| Primary category | cs.SE |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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