Summary of Fairness Definitions in Language Models Explained, by Thang Viet Doan et al.
Fairness Definitions in Language Models Explained
by Thang Viet Doan, Zhibo Chu, Zichong Wang, Wenbin Zhang
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a systematic survey to clarify the definitions of fairness as they apply to Language Models (LMs). Despite LMs’ exceptional performance across various Natural Language Processing (NLP) tasks, they can inherit and amplify societal biases related to sensitive attributes. To address this limitation, researchers have proposed various fairness notions for LMs. However, the lack of clear agreement on which definition to apply in specific contexts and the complexity of understanding these definitions can impede further progress. The authors provide a comprehensive overview of existing fairness notions in LMs, categorizing them based on their foundational principles and operational distinctions. Experiments illustrate each definition’s practical implications and outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make Language Models fairer. Right now, these models can be biased towards certain groups or people, which is a problem. Researchers have come up with different ways to measure fairness in LMs, but there isn’t one clear answer. This makes it hard to decide which approach to use in specific situations. The authors of this paper want to help by providing a clear overview of all these fairness notions and how they work. They also show examples of each definition in action, so we can see what the practical implications are. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
