Summary of The Impact Of Unstated Norms in Bias Analysis Of Language Models, by Farnaz Kohankhaki et al.
The Impact of Unstated Norms in Bias Analysis of Language Models
by Farnaz Kohankhaki, D. B. Emerson, Jacob-Junqi Tian, Laleh Seyyed-Kalantari, Faiza Khan Khattak
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 research paper investigates biases in large language models, exploring how their performance changes when group membership is explicitly stated. Counterfactual bias evaluation methods are commonly used to quantify these biases, but the authors find that template-based probes can lead to inaccurate measurements. By comparing text associated with different racial groups, they show that LLMs tend to incorrectly categorize text as negative when it’s related to White people more often than other groups. The study suggests this might be due to a mismatch between norms in pre-training text and the templates used for bias measurement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language models can unfairly treat certain groups of people. Right now, we use special tests called counterfactual bias evaluations to check if these models are biased. But what if these tests are giving us fake results? That’s what the scientists in this study wanted to find out. They compared text related to different racial groups and found that language models often mistakenly think negative things about White people more often than other groups. This might be because the models learned from texts where some norms weren’t explicitly stated, but we still see those norms when we test the models. |
