Update contamination_report.csv

## What are you reporting:
- [X ] Evaluation dataset(s) found in a pre-training corpus. (e.g. COPA found in ThePile)
- [ ] Evaluation dataset(s) found in a pre-trained model. (e.g. FLAN T5 has been trained on ANLI)

**Evaluation dataset(s)**: Name(s) of the evaluation dataset(s). If available in the HuggingFace Hub please write the path (e.g. `uonlp/CulturaX`), otherwise provide a link to a paper, GitHub or dataset-card.

**Contaminated model(s)**: Name of the model(s) (if any) that have been contaminated with the evaluation dataset. If available in the HuggingFace Hub please list the corresponding paths (e.g. `allenai/OLMo-7B`).

**Contaminated corpora**: Name of the corpora used to pretrain models (if any) that have been contaminated with the evaluation dataset. If available in the HuggingFace hub please write the path (e.g. `CohereForAI/aya_dataset`)

**Contaminated split(s)**: If the dataset has Train, Development and/or Test splits please report the contaminated split(s). You can report a percentage of the dataset contaminated.


## Briefly describe your method to detect data contamination

- [ ] Data-based approach
- [ ] Model-based approach

Description of your method, 3-4 sentences. Evidence of data contamination (Read below):

#### Data-based approaches
Data-based approaches identify evidence of data contamination in a pre-training corpus by directly examining the dataset for instances of the evaluation data. This method involves algorithmically searching through a large pre-training dataset to find occurrences of the evaluation data. You should provide evidence of data contamination in the form: "dataset X appears in line N of corpus Y," "dataset X appears N times in corpus Y," or "N examples from dataset X appear in corpus Y."

#### Model-based approaches

Model-based approaches, on the other hand, utilize heuristic algorithms to infer the presence of data contamination in a pre-trained model. These methods do not directly analyze the data but instead assess the model's behavior to predict data contamination. Examples include prompting the model to reproduce elements of an evaluation dataset to demonstrate memorization (i.e https://hitz-zentroa.github.io/lm-contamination/blog/), or using perplexity measures to estimate data contamination (). You should provide evidence of data contamination in the form of evaluation results of the algorithm from research papers, screenshots of model outputs that demonstrate memorization of a pre-training dataset, or any other form of evaluation that substantiates the method's effectiveness in detecting data contamination. You can provide a confidence score in your predictions.

## Citation

Is there a paper that reports the data contamination or describes the method used to detect data contamination?

URL: `https://aclanthology.org/2023.findings-emnlp.722/`
Citation: `@inproceedings{...`

contamination_report.csv

@@ -1,4 +1,5 @@
 Evaluation Dataset;Contaminated Source;Model or corpus;Train Split;Development Split;Test Split;Approach;Citation;PR Link
 conll2003;google/gemma-7b;model;1.0;1.0;1.0;model-based;https://hitz-zentroa.github.io/lm-contamination/blog/;
 conll2003;EleutherAI/the_pile_deduplicated;corpus;1.0;1.0;1.0;data-based;https://aclanthology.org/2023.findings-emnlp.722/;www.google.com
-Test;lololol;corpus;1.0;1.0;1.0;data-based;https://arxiv.org/abs/2310.03668;
+Test;lololol;corpus;1.0;1.0;1.0;data-based;https://arxiv.org/abs/2310.03668;
+a;aaa