Memorization Diagnostics for Code LLMs Should be Scale-Aware
The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread memorization, evaluating the underlying probing techniques across dense architectures reveals a severe breakdown in their utility at scale. Traditional encoder-style probes using perturbations such as synonym fuzzing or dead-code insertion struggle to expose memorization in scaled models, even on known-contaminated benchmarks, and decoder-style probes that rely on log probabilities show similar performance degradation. The specific mode of failure for these probes, particularly why such techniques disrupt smaller ...
| Authors | Prateek Kumar Rajput, Abdoul Aziz Bonkoungou, Alberick Euraste Djiré et al. |
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| Primary category | cs.SE |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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