Challenges in Evaluating Language Models for Code Generation
Code generation has become an important area for evaluating and deploying Large Language Models (LLMs). However, current coding benchmarks have saturated with solution rates above 90%, indicating 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
Models must be able to reason across various settings and create original algorithms specific to each challenge scenario to succeed in USACO. Despite this, even the most sophisticated language model, GPT-4, only manages an 8.7% zero-shot pass rate@1.
The benchmark provides official analyses, reference code solutions, high-quality unit tests, and instructional materials to facilitate the investigation of more inference techniques for competitive programming. 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 has been introduced, offering 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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