Self-invoking code benchmarks help developers decide which LLMs to use

OpenAI and Yale researchers have developed new benchmarks to evaluate how well large language models (LLMs) handle complex programming tasks that mirror real-world software development scenarios.

The innovation: Self-invoking code generation benchmarks test LLMs’ ability to both write new code and reuse previously generated code to solve increasingly complex programming problems.

• Traditional benchmarks like HumanEval and MBPP only test simple, isolated coding tasks
• The new benchmarks, HumanEval Pro and MBPP Pro, require models to build upon their own generated solutions
• These tests better reflect real programming scenarios where developers must understand and reuse existing code

Key findings: Current LLMs struggle significantly more with self-invoking code generation compared to traditional coding benchmarks.

• OpenAI’s o1-mini model achieves 96.2% accuracy on standard HumanEval but only 76.2% on HumanEval Pro
• Instruction fine-tuning, which typically improves performance on simple tasks, shows diminishing returns on self-invoking code generation
• Even advanced models like GPT-4, Claude 3.5, and others demonstrated notable performance gaps

Technical implementation: The researchers developed an automated approach to create these new benchmarks efficiently.

• The system uses advanced LLMs to generate self-invoking problems based on existing benchmark tasks
• It automatically verifies solutions through code execution and test cases
• This automation reduces the need for manual code review while maintaining benchmark quality

Broader context: These benchmarks fill an important gap in evaluating AI coding capabilities.

• They sit between simple coding tests and complex end-to-end software engineering benchmarks like SWE-Bench
• They specifically measure an LLM’s ability to reason about and reuse code within a module
• This capability is particularly relevant for AI-assisted programming tools that support human developers

Future implications: While current LLMs excel at generating isolated code snippets, their struggles with self-invoking code generation highlight the need for new training approaches that better mirror real-world programming scenarios.

• The findings suggest that existing instruction-based fine-tuning methods may need to be reconsidered
• The benchmarks provide clear metrics for measuring progress in this crucial area
• Results indicate that significant improvements in LLM architecture or training may be needed to match human-level programming capabilities

Looking ahead: These new benchmarks reveal important limitations in current AI coding assistants while providing a clearer roadmap for developing more capable programming AI tools that can truly support complex software development tasks.