6 papers
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code
Alberick Euraste Djire, Iyiola E. Olatunji, Melissa Tessa +3
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We…
Do Not Copy/Paste: Soft Barriers for Copying in AI-Assisted Programming
Iyiola E. Olatunji, Alberick Euraste Djire, Jacques Klein +1
Copying a function from a chat window into an editor takes less than a second. For many uses of AI coding tools, that speed is the point; in settings such as programming education,…
Memorization Diagnostics for Code LLMs Should be Scale-Aware
Prateek Kumar Rajput, Abdoul Aziz Bonkoungou, Alberick Euraste Djiré +6
The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread me…
Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation…
Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study
Serge Lionel Nikiema, Jordan Samhi, Micheline Bénédicte Moumoula +4
This research addresses a fundamental question in AI: whether large language models truly understand concepts or simply recognize patterns. The authors propose bidirectional reason…
Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis
Albérick Euraste Djiré, Abdoul Kader Kaboré, Earl T. Barr +2
While Large Language Models (LLMs) achieve remarkable performance through training on massive datasets, they can exhibit concerning behaviors such as verbatim reproduction of train…