Summary of Assessing Bias in Metric Models For Llm Open-ended Generation Bias Benchmarks, by Nathaniel Demchak et al.
Assessing Bias in Metric Models for LLM Open-Ended Generation Bias Benchmarks
by Nathaniel Demchak, Xin Guan, Zekun Wu, Ziyi Xu, Adriano Koshiyama, Emre Kazim
First submitted to arxiv on: 14 Oct 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 The paper examines the biases present in open-generation benchmarks used to evaluate social biases in Large Language Models (LLMs). The researchers analyzed two such benchmarks, BOLD and SAGED, using the MGSD dataset. They conducted two experiments: one that measures prediction variations across demographic groups by altering stereotype-related prefixes, and another that uses explainability tools (SHAP) to validate the observed biases. The results reveal unequal treatment of demographic descriptors, highlighting the need for more robust bias metric models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how fair it is to use certain tests to check if language models have social biases. They want to make sure these tests don’t have their own biases that can affect the results. To do this, they used a special dataset and did two different experiments. One experiment changed words to see how the model’s predictions change based on demographics. The other experiment used a tool to explain why the model made certain predictions. The results showed that the model doesn’t treat all demographic groups equally. |
