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New framework detects data leakage in code LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed SrDetection, a novel framework designed to identify data leakage in code large language models (Code LLMs). This self-referential approach generates variations of benchmark samples to detect when a model's performance is artificially inflated due to prior exposure to the benchmark data. SrDetection offers improvements in both gray-box and black-box settings, outperforming existing methods and revealing specific leakage patterns across various Code LLMs and benchmarks. AI

IMPACT This framework could lead to more reliable evaluations of code LLMs, ensuring that benchmark performance accurately reflects true capabilities rather than memorization.

RANK_REASON The cluster describes a research paper detailing a new framework for detecting data leakage in code LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework detects data leakage in code LLMs

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The cluster describes a research paper detailing a new framework for detecting data leakage in code LLMs.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Shuaimin Li, Liyang Fan, Zeyang Li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang ·

    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…

  2. arXiv cs.CL TIER_1 English(EN) · Min Yang ·

    SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models

    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…

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