Summary of Different Bias Under Different Criteria: Assessing Bias in Llms with a Fact-based Approach, by Changgeon Ko et al.
Different Bias Under Different Criteria: Assessing Bias in LLMs with a Fact-Based Approach
by Changgeon Ko, Jisu Shin, Hoyun Song, Jeongyeon Seo, Jong C. Park
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper addresses the issue of large language models (LLMs) reflecting real-world biases by introducing a novel metric to assess bias using fact-based criteria and real-world statistics. The proposed method aims to provide a distinct perspective from equality-based approaches, which are often challenged by differing perspectives on equality and pluralism. To evaluate LLMs, the authors conducted a human survey showing that humans tend to perceive LLM outputs more positively when they align closely with real-world demographic distributions. The results highlight the need for multi-perspective assessment of model bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models fairer by using facts and real-life data. Right now, language models often reflect the biases we see in the world, which can be a problem. Some people think fairness means treating everyone equally, while others believe it’s more important to represent different perspectives. The authors of this paper want to find a way to measure bias that doesn’t rely on these subjective ideas. They did a survey with humans and found that when language models match real-life demographics, people tend to like the results better. This shows that we need to look at model bias from multiple angles. |
