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Our research investigates the potential of Large-scale Language Models (LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary classification task.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
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2022
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Le Quy, T., Roy, A., Iosifidis, V., Zhang, W., Ntoutsi, E.: A survey on datasets for fairness-aware machine learning. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12
2022
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Naghiaei, M., Rahmani, H.A., Deldjoo, Y.: Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 770–779 (2022)
2022
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2022
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35
2022
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2023
Cited alongside, same era.
Amigó, E., Deldjoo, Y., Mizzaro, S., Bellogín, A.: A unifying and general account of fairness measurement in recommender systems. Information Processing & Management 60
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Clavié, B., Ciceu, A., Naylor, F., Soulié, G., Brightwell, T.: Large language models in the workplace: A case study on prompt engineering for job type classification. In: International Conference on Applications of Natural Language to Information Systems. pp. 3–17. Springer (2023)
2023
Cited alongside, same era.
Li, Y., Zhang, Y.: Fairness of chatgpt. arXiv preprint arXiv:2305.18569 (2023)
2023
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2023
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2023
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2023
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Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., Zanzonelli, D.: Fairness in recommender systems: research landscape and future directions. User Modeling and User-Adapted Interaction pp. 1–50 (2023)
2023
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2023
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