Papers with causal formulation
Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models can cause harmful, human-like biases against various demographics. |
| Approach: | They propose a causal formulation for bias measurement in generative language models based on a list of desiderata for designing robust bias benchmarks and a bias-measuring procedure to investigate occupational gender bias. |
| Outcome: | The proposed framework is generalizable and can be extended to include other datasets. |