Code generation is a crucial area for evaluating and deploying Large Language Models (LLMs). However, existing coding benchmarks have become too easy, with solution rates above 90%. This highlights the need for more challenging benchmarks.
Introducing USACO Benchmark
USACO is a constructed coding benchmark with 307 difficult tasks from previous USA Computing Olympiad contests. It offers a wide range of challenges that require algorithmic, mathematical, and common sense expertise to solve.
Assessment and Improvement
For success in USACO, models must be able to reason across various settings and create original algorithms specific to each challenge scenario. Even the most sophisticated language model, GPT-4, only manages an 8.7% zero-shot pass rate@1. However, strategies combining retrieval and self-reflection have greatly improved performance, more than tripling the zero-shot solve rate of GPT-4.
Human-in-the-Loop Study
A human-in-the-loop study found that giving GPT-4 tailored suggestions made it solve 13 out of 15 previously unsolvable problems, outperforming all previous models and methods examined.
Key Contributions
The USACO benchmark offers carefully selected test cases, problem analysis, and resources for thorough assessment. LLM inference techniques have been developed and analyzed specifically for Olympiad programming challenges. The new study evaluates the potentials and constraints of LLMs for Olympiad programming, revealing hidden differences between models.
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