Papers with contamination detection approaches
Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges (2025.coling-main)
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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 . |
| Approach: | They evaluate five different methods for detecting data contamination in large language models . they find that current methods have non-trivial limitations in their assumptions and practical applications . |
| Outcome: | The proposed methods have non-trivial limitations and difficulties in detecting contamination . the authors highlight the complexity of contamination detection in advanced LLMs . |
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)
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| Challenge: | Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data. |
| Approach: | They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution. |
| Outcome: | The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution. |