SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models
arXiv:2606.29815v1 Announce Type: new Abstract: Evaluating code large language models (Code LLMs) requires reliable detection of data leakage, where benchmark performance is artificially inflated by exposure to benchmark data during pre-training. Existing approaches either assume access to proprietary training corpora, rely on brittle heuristics such as timestamp filtering, or use external reference sets with manually tuned, non-generalizable thresholds. To address these limitations, we introduce \textbf{SrDetection}, a unified \textbf{s}elf-\textbf{r}eferential leakage detection framework for
The rapid development and deployment of Code LLMs necessitate robust mechanisms for evaluating their integrity and preventing artificial performance inflation.
Reliable data leakage detection is crucial for accurately assessing the true capabilities and security implications of Code LLMs, impacting investment and development strategies.
The introduction of a self-referential framework offers a more generalized and less brittle approach to identifying data leakage compared to previous methods.
- · Code LLM developers
- · AI evaluation firms
- · Software engineering sector
- · Code LLMs with undetected leakage
- · Developers relying on inflated benchmarks
SrDetection provides a standardized method for evaluating the true performance of Code LLMs.
Improved evaluation will lead to more robust and trustworthy Code LLMs and better allocation of research resources.
The widespread adoption of such frameworks could accelerate the responsible development and integration of AI in critical software infrastructure.
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Read at arXiv cs.CL