fairness ↔ Large Language Models

Relations (1)

related 2.58 — strongly supporting 5 facts

Large Language Models and fairness are linked through academic research that examines bias and ethical challenges within these systems, as evidenced by multiple surveys [1], [2], and [3]. Furthermore, the integration of these concepts is highlighted by the ongoing scholarly debate regarding the difficulty of formalizing fairness definitions within the context of Large Language Models [4] and [5].

Facts (5)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 5 facts

referenceThe paper 'Bias and fairness in large language models: a survey' provides a comprehensive overview of bias and fairness issues within large language models.

claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).

referenceThe paper 'Fairness in large language models: a taxonomic survey' was published in the ACM SIGKDD explorations newsletter 26 (1), pp. 34–48.

referenceThe paper 'A survey on fairness in large language models' is available as arXiv preprint arXiv:2308.10149.

claimIn the current landscape of Large Language Models, definitions of robustness, fairness, and privacy are often ambiguous and lack simple closed-form mathematical representations compared to traditional machine learning.