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Data contamination in evaluation is getting increasingly prevalent with theemergence of language models pre-trained on super large, automatically crawledcorpora. This problem leads to significant challenges in the accurateassessment of model capabilities and generalisations. In this paper, we proposeLatestEval, an automatic method that leverages the most recent texts to createuncontaminated reading comprehension evaluations. LatestEval avoids datacontamination by only using texts published within a recent time window,ensuring no overlap with the training corpora of pre-trained language models.We develop the LatestEval automated pipeline to 1) gather the latest texts; 2)identify key information, and 3) construct questions targeting the informationwhile removing the existing answers from the context. This encourages models toinfer the answers themselves based on the remaining context, rather than justcopy-paste. Our experiments demonstrate that language models exhibit negligiblememorisation behaviours on LatestEval as opposed to previous benchmarks,suggesting a significantly reduced risk of data contamination and leading to amore robust evaluation. Data and code are publicly available at:https://github.com/liyucheng09/LatestEval.

LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction