Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications

Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju


Abstract
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is important to explore the following questions: what sources of information do LLMs draw upon when making classifications for tabular tasks; whether and to what extent are LLM classifications for tabular data influenced by social biases and stereotypes; and what are the consequential implications for fairness?Through a series of experiments, we delve into these questions and show that LLMs tend to inherit social biases from their training data which significantly impact their fairness in tabular classification tasks. Furthermore, our investigations show that in the context of bias mitigation, though in-context learning and finetuning have a moderate effect, the fairness metric gap between different subgroups is still larger than that in traditional machine learning models, such as Random Forest and shallow Neural Networks. This observation emphasizes that the social biases are inherent within the LLMs themselves and inherited from their pretraining corpus, not only from the downstream task datasets. Besides, we demonstrate that label-flipping of in-context examples can significantly reduce biases, further highlighting the presence of inherent bias within LLMs.
Anthology ID:
2024.naacl-long.198
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3603–3620
Language:
URL:
https://aclanthology.org/2024.naacl-long.198
DOI:
Bibkey:
Cite (ACL):
Yanchen Liu, Srishti Gautam, Jiaqi Ma, and Himabindu Lakkaraju. 2024. Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3603–3620, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications (Liu et al., NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-long.198.pdf
Copyright:
 2024.naacl-long.198.copyright.pdf