| Challenge: | a large number of large language models (LLMs) are being evaluated for their performance, but their reliability is threatened by test set contamination. |
| Approach: | They propose a framework that decontaminates large language models by applying small perturbations to the input embedding space. |
| Outcome: | The proposed framework achieves strong decontamination effectiveness while incurring minimal degradation in benign utility. |
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Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
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| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Investigating Data Contamination in Modern Benchmarks for Large Language Models (2024.naacl-long)
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| Challenge: | Existing evaluation benchmarks for large language models are inflated and inconsistent with actual performance. |
| Approach: | They propose a retrieval-based system to explore potential overlaps between benchmarks and pretraining corpora and a protocol to investigate testset slot guessing. |
| Outcome: | The proposed method exploits overlaps between evaluation benchmarks and pretraining corpora and masks a wrong answer in a multiple choice question and prompts the model to fill in the gap. |
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. |
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark (2023.findings-emnlp)
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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 . |
| Approach: | They argue that the evaluation of NLP tasks using annotated benchmarks is in trouble . they define different levels of data contamination and propose a community effort . |
| Outcome: | The proposed measures should detect when data from a benchmark was exposed to a model and flag papers with conclusions compromised by data contamination. |
DCR: Quantifying Data Contamination in LLMs Evaluation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) memorize evaluation data during training, inflating performance metrics and undermining genuine generalization assessment. |
| Approach: | They propose a framework to detect and quantify benchmark data contamination (BDC) by synthesizing contamination scores via a fuzzy inference system. |
| Outcome: | The proposed framework detects and quantifies BDC risk across semantic, informational, data, and label levels. |
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
| Challenge: | In the era of evaluating large language models, data contamination is an increasingly prominent concern . static benchmarking has been used for evaluation, but there are limitations of *dynamic* benchmarks . |
| Approach: | They propose a series of optimal design principles for *dynamic* benchmarking and analyze the limitations of existing *static* benchmarks. |
| Outcome: | The proposed benchmarks highlight a critical gap in the evaluation of LLMs. |
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. |
| Approach: | They perform extensive analysis on the factors that affect model memorization and generalization, such as model size, problem difficulty, and question length. |
| Outcome: | The results show that models perform better on the subset of the benchmarks where similar solutions are seen during training. |
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025.acl-long)
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Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang
| Challenge: | Existing studies solve this challenge by updating benchmarks with newly collected data, but they fail to guarantee contamination-free evaluation as the newly collected knowledge may contain pre-existing knowledge. |
| Approach: | They propose an automated anti-leakage benchmarking framework that builds and updates benchmarks without human labor instead of using newly collected data. |
| Outcome: | The proposed framework significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. |
Data Contamination Can Cross Language Barriers (2024.emnlp-main)
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| Challenge: | Existing methods to detect contamination of public benchmarks are too superficial to reflect deeper forms of contamination. |
| Approach: | They propose generalization-based approaches to unmask a cross-lingual form of contamination that inflates LLMs’ performance while evading current detection methods. |
| Outcome: | The proposed model outperforms existing detection methods while avoiding contamination of public benchmarks in the pre-training data. |
Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) excel on public benchmarks, but high scores may mask overreliance on dataset-specific surface cues rather than true language understanding. |
| Approach: | They propose a meta-evaluation framework that systematically rephrases benchmark inputs to detect overfitting. |
| Outcome: | The proposed framework detects performance degradation indicative of superficial pattern reliance on dataset-specific cues and distortion levels. |