Benchmark Data Contamination of Large Language Models: A Survey, AI(16)

Benchmark Data Contamination of Large Language Models: A Survey
Cheng Xu, Shuhao Guan, Derek Greene, M-Tahar Kechadi
https://arxiv.org/abs/2406.04244v1

# REFERENCES
[1] Charu C. Aggarwal. 2018. Opinion Mining and Sentiment Analysis. Springer International Publishing, Cham, 413–434.
https://doi.org/10.1007/978-3-319-73531-3_13

[2] Abdulmohsen Al-Thubaity, Sakhar Alkhereyf, Hanan Murayshid, Nouf Alshalawi, Maha Omirah, Raghad Alateeq,
Rawabi Almutairi, Razan Alsuwailem, Manal Alhassoun, and Imaan Alkhanen. 2023. Evaluating ChatGPT and Bard AI on Arabic Sentiment Analysis. In Proceedings of ArabicNLP 2023, Hassan Sawaf, Samhaa El-Beltagy, Wajdi
Zaghouani, Walid Magdy, Ahmed Abdelali, Nadi Tomeh, Ibrahim Abu Farha, Nizar Habash, Salam Khalifa, Amr Keleg, Hatem Haddad, Imed Zitouni, Khalil Mrini, and Rawan Almatham (Eds.). Association for Computational Linguistics, Singapore (Hybrid), 335–349. https://doi.org/10.18653/v1/2023.arabicnlp-1.27, Vol. 1, No. 1, Article . Publication date: June 2024.

[3] Mussa Aman. 2024. Large Language Model Based Fake News Detection. Procedia Computer Science 231 (2024), 740–745. https://doi.org/10.1016/j.procs.2023.12.144 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (EUSPN/ICTH 2023).

[4] Anthropic. 2024. Introducing the next generation of Claude. https://www.anthropic.com/news/claude-3-family

[5] Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan. 2021. A General Language Assistant as a Laboratory for Alignment. https://arxiv.org/pdf/2112.00861 [cs.CL]

[6] Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, and Lei Hou. 2023. Benchmarking Foundation Models with Language-Model-as-anExaminer. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=IiRHQ7gvnq

[7] Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, and Ondrej Dusek. 2024. Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), Yvette Graham and Matthew Purver (Eds.). Association for Computational Linguistics, St. Julian’s, Malta, 67–93. https://aclanthology.org/2024.eacl-long.5

[8] Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, and Pascale Fung. 2023. A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), Jong C. Park, Yuki Arase, Baotian Hu, Wei Lu, Derry Wijaya, Ayu Purwarianti, and Adila Alfa Krisnadhi (Eds.). Association for Computational Linguistics, Nusa Dua, Bali, 675–718. https://doi.org/10.18653/v1/2023.ijcnlp-main.45

[9] Rachel Bawden and François Yvon. 2023. Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOM. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, and Helena Moniz (Eds.). European Association for Machine Translation, Tampere, Finland, 157–170. https://aclanthology.org/2023.eamt-1.16

[10] Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. 2000. A Neural Probabilistic Language Model. In Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp (Eds.), Vol. 13. MIT Press. https://proceedings.neurips.cc/paper_files/paper/2000/file/728f206c2a01bf572b5940d7d9a8fa4c-Paper.pdf

[11] James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. 2023. Improving image generation with better captions. https://cdn.openai.com/papers/dall-e-3.pdf

[12] Terra Blevins and Luke Zettlemoyer. 2022. Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 3563–3574. https://doi.org/10.18653/v1/2022.emnlp-main.233

[13] Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020. Language (Technology) is Power: A Critical Survey of “Bias” in NLP. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (Eds.). Association for Computational Linguistics, Online, 5454–5476. https://doi.org/10.18653/v1/2020.acl-main.485

[14] Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, and Rich Caruana. 2024. Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models. https://arxiv.org/pdf/2404.06209 [cs.LG]

[15] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901. https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf

[16] Jialun Cao, Wuqi Zhang, and Shing-Chi Cheung. 2024. Concerned with Data Contamination? Assessing Countermeasures in Code Language Model. https://arxiv.org/pdf/2403.16898 [cs.SE]

[17] Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramèr, Borja Balle, Daphne
Ippolito, and Eric Wallace. 2023. Extracting Training Data from Diffusion Models. In 32nd USENIX Security Symposium (USENIX Security 23). USENIX Association, Anaheim, CA, 5253–5270. https://www.usenix.org/conference/