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Study finds data contamination has nuanced impact on code intelligence models

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates the impact of data contamination on code intelligence models, specifically examining how different types of contamination affect performance evaluations. The research tested various models including RoBERTa, GPT-2, LLaMA, and StarCoder across code translation, generation, and summarization tasks in Java and Python. Findings suggest that while paired contamination does not significantly overestimate performance in pre-training/fine-tuning paradigms, it does affect LLMs during direct inference or small-scale fine-tuning. AI

IMPACT Provides new insights into the evaluation and deployment of code intelligence models, challenging conventional beliefs about data contamination.

RANK_REASON Research paper published on arXiv detailing empirical study of data contamination in code intelligence models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Study finds data contamination has nuanced impact on code intelligence models

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Research paper published on arXiv detailing empirical study of data contamination in code intelligence models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhen Yang, Hongyi Lin, Yifan He, Junqi Wang, Zeyu Sun, Shuo Liu, Jie Xu, Pengpeng Wang, Zhongxing Yu, Qingyuan Liang ·

    Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence

    arXiv:2506.02791v4 Announce Type: replace-cross Abstract: In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models (PLMs) and Large Language Models (LLMs) h…

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