Data Contamination Report from the 2024 CONDA Shared Task (2024)

Abstract: The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community.

## introduction data contamination, where evaluation data is inadvertently included in pre-training corpora of largescale models, and language models (lms) in particular, has become a concern in recent times @xcite . the growing scale of both models and data, coupled with massive web crawling, has led to the inclusion of segments from evaluation benchmarks in the pretraining data of lms @xcite @xcite . the scale of internet data makes it difficult to prevent this contamination from happening, or even detect when it has happened @xcite . crucially, when evaluation data becomes part of pre-training data, it introduces biases and can artificially inflate the performance of lms on specific tasks or benchmarks @xcite @xcite . this poses a challenge for fair and unbiased evaluation of models, as their performance may not accurately reflect their generalization capabilities @xcite . and similarly to pre-training contamination, the contamination can also occur during the fine-tuning stage even after a model has been deployed as an @xcite . although a growing number of papers and stateof-the-art models mention issues of data contamination @xcite @xcite @xcite , there is little in the way of organized and compiled knowledge about real, documented cases of contamination in practice @xcite . addressing data contamination is a shared responsibility among researchers, developers, and the broader community. ## methodology and previous work collecting all the contamination evidence -or lack of it-was done openly, through pull requests, and subject to discussions before the admission. contributors were asked to fill in the information about several aspects, such as the contaminated resource (a training corpus or model), the evaluation dataset which was found in the contaminated source, a breakdown of the percentage of contamination found in each split of the dataset (train, development, and test), an optional reference to a g p t -4 g p t -3 g p t -3 . 5 f l a n p a l m m i s t r a l -v 0 . 1 g p t -3 . 5 t u r b o p a l m 2 l l a m a 2 g l a m a q u i l a 2 l l e m m a q w e n -1 c h a t g l m 3 i n t e r n l m 2 0 2 4 6 8 10 12 14 16 18 20 number of contamination reports positive reports negative reports figure 3: number of test sets reported for each pretrained model. the contributions provided the huggingface hub id of models, corpus, and datasets when possible. in addition, contributors must provide the evidence or a reference to the scientific paper that reported the evidence originally. figure 1 shows the taxonomy of the papers that reported contamination evidence in the shared task. 3 we split the these methods into two: data-based and model-based approaches. data-based approaches. are methods that inspect the pre-training corpora to find contamination evidence. data-based approaches typically involve string or sub-string matching techniques such as 13-gram overlap @xcite , 50-character overlap @xcite or even full-string overlap @xcite . in figure 1 we differentiate between proprietary and open data. papers that fall in the category of proprietary data are usually llms technical reports that run post-hoc data contamination evaluations to identify and remove evaluation instances that appear in the pre-training corpora @xcite @xcite . papers that fall in the open data category usually involve corpus analysis tools @xcite or llms with publicly available pre-training data @xcite . model-based approaches. are those methods that 3 note that there are many other works on data contamination detection. in this report we focus on works that were used to detect contamination for this report. we leave a more detailed coverage survey for future work. the collected evidences come from different approaches and sources, making them hardly comparable. for transparency, we included in the database information about the source of the evidence and the link to the discussion. we encourage the users to assess how the evidence was collected for their datasets of interest. ## compilation of evidence the report includes 42 contaminated sources (training corpora or models), 91 datasets, and 566 contamination entries, including 432 contamination events (20 train-set, 95 dev-set, 317 test-set) and 144 non-contamination events, where a contamination event is taken as any report above 0% of contamination. the database contains, for each split (train, dev, and test) of each evaluation dataset, what percentage was found to be contaminated by a subset of the contamination sources (corpora or models). we analyze separately the contaminated corpora and models. ## analysis of the reported data in this section, we analyze the reported entries to understand the report's data better. reported tasks. figure 4 shows the percentage of data contamination per task. we use the task_id assigned to each dataset in the hugging face hub. text-scoring, qa, and multiple-choiceqa are among the most contaminated task types. figure 5 shows the number of downloads for every dataset in the report. we measure the total num- ## conclusions data contamination has become a significant concern in recent times. consequently, a growing number of papers and state-of-the-art models mention issues of data contamination. in the conda 2024 shared task on evidence of data contamination, we have collected and compiled a comprehensive database of available evidence on data contamination in currently available datasets and models. this report includes 566 contamination entries over 91 contaminated sources from a total of 23 contributors. with this shared task, we provide a structured, centralized platform for contamination evidence collection to help the community understand the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. given the large exploration space, this report does not cover all cases, but a small sample that were reported during our shared task period, in the midst of 2024. we welcome further submissions to the database, and plan to keep this database up-to-date as it provides a valuable source of information for the research community.