Summary of Fairbelief — Assessing Harmful Beliefs in Language Models, by Mattia Setzu et al.
FairBelief – Assessing Harmful Beliefs in Language Models
by Mattia Setzu, Marta Marchiori Manerba, Pasquale Minervini, Debora Nozza
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: The paper proposes FairBelief, an analytical approach to assess the beliefs embedded in Language Models (LMs), which can impact their predictions. By leveraging prompting, the authors study several state-of-the-art LMs across different axes, including model scale and likelihood, on a fairness dataset designed to quantify hurtfulness. Their findings reveal that English LMs, despite high performance on natural language processing tasks, exhibit hurtful beliefs about specific genders. The study highlights the importance of careful fairness auditing when integrating LMs into real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper looks at how Language Models (LMs) think and decide things. Researchers found that some LMs can have unfair or hurtful thoughts, which affect their predictions. They developed a way to study these thoughts and discovered that even the best-performing LMs can be biased against certain groups. The study shows us why we need to carefully check how LMs work before using them in real life. |
Keywords
» Artificial intelligence » Likelihood » Natural language processing » Prompting
