Summary of Evaluating Gender, Racial, and Age Biases in Large Language Models: a Comparative Analysis Of Occupational and Crime Scenarios, by Vishal Mirza et al.
Evaluating Gender, Racial, and Age Biases in Large Language Models: A Comparative Analysis of Occupational and Crime Scenarios
by Vishal Mirza, Rahul Kulkarni, Aakanksha Jadhav
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A recent study examines the pervasive problem of bias in Large Language Models (LLMs), a crucial issue affecting their usability, reliability, and fairness. The paper investigates strategies to mitigate bias, including debiasing layers, specialized reference datasets, and reinforcement learning with human feedback. These techniques have been integrated into the latest LLMs. The study evaluates gender, age, and racial bias in occupational and crime scenarios across four leading LLMs released in 2024: Gemini 1.5 Pro, Llama 3 70B, Claude 3 Opus, and GPT-4o. Findings reveal that LLMs often depict female characters more frequently than male ones in various occupations, showing a significant deviation from US BLS data. In crime scenarios, deviations are observed for gender, race, and age. The study highlights the limitations of current bias mitigation techniques and underscores the need for more effective approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Large Language Models (LLMs) can be biased in what they say about people and things. Researchers want to make sure LLMs are fair and don’t show stereotypes. They’re trying new ways to fix this problem, like special training data or getting feedback from humans. The study checks four popular LLMs to see if they have biases when talking about jobs, crimes, and other things. It found that these models often say nice things about women but not men in certain jobs. When it comes to crime, the models are a bit unfair too. This shows us that we need better ways to make sure LLMs aren’t biased. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Llama » Reinforcement learning
