VentureBeat January 10, 2025
As large language models (LLMs) continue to improve at coding, the benchmarks used to evaluate their performance are steadily becoming less useful.
That’s because though many LLMs have similar high scores on these benchmarks, understanding which ones to use on specific software development projects and enterprises can be difficult.
A new paper by Yale University and Tsinghua University presents a novel method to test the ability of models to tackle “self-invoking code generation” problems that require reasoning, generating code, and reusing existing code in problem-solving.
Self-invoking code generation is much more similar to realistic programming scenarios than benchmark tests are, and it provides a better understanding of current LLMs’ ability to solve real-world coding problems.
Self-invoking code generation
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