PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models are trained on vast amounts of data, which may unintentionally or intentionally include data from commonly used benchmarks. |
| Approach: | They propose a set of requirements that practical contamination detection methods should follow to effectively detect benchmark contamination in large language models. |
| Outcome: | The proposed method detects whether the model is significantly more confident under the original benchmark. |
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| Challenge: | Existing methods for detecting data contamination in large language models have limitations and limitations . data contamination occurs when test or evaluation data is exposed to the model during its training phases . |
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| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
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| Challenge: | Existing evaluation benchmarks for large language models are inflated and inconsistent with actual performance. |
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| Challenge: | Existing methods for evaluating large language models using annotated benchmarks are in trouble . data contamination can cause wrong scientific conclusions being published . |
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Benchmarking Large Language Models Under Data Contamination: A Survey from Static to Dynamic Evaluation (2025.emnlp-main)
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Simin Chen, Yiming Chen, Zexin Li, Yifan Jiang, Zhongwei Wan, Yixin He, Dezhi Ran, Tianle Gu, Haizhou Li, Tao Xie, Baishakhi Ray
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Data Contamination Can Cross Language Barriers (2024.emnlp-main)
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| Challenge: | Data contamination is a problem in Large language models due to the reliance on extensive internet-derived training corpora. |
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Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models (2024.acl-long)
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| Challenge: | Recent studies have shown that large language models are contaminated with data from pretraining and finetuning tasks. |
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Data Contamination Calibration for Black-box LLMs (2024.findings-acl)
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| Challenge: | Despite the rapid advancements of Large Language Models, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination. |
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