Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models
If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude. We test this zero-shot on current commercial models. Five LLMs generate solutions to MBPP, HumanEval, and DS-1000, seven more to MBPP, and models act as evaluators in four tasks: picking their own solution from a pair, judging whether a single solution is their own, identifying which of two solutions a named model wrote, and judging quality blind. In the single-solution task, balanced accuracy is 49-58% for all 15 model-benchmark combinations, while raw accuracy (38-67%) mostly reflects how readily a model claims authorship. In the pairwise ta...
| Authors | Ehsan Barkhordar, Surendrabikram Thapa |
|---|---|
| Primary category | cs.AI |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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