Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts (2022.findings-emnlp)
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| Challenge: | Past studies have shown biases in natural language generation systems but there has been little work on evaluating the bias evaluation approaches. |
| Approach: | They propose a method for evaluating biases in natural language generation systems by paraphrasing syntactic prompts with different syntaktic structures and paraphrazing them to evaluate demographic bias. |
| Outcome: | The proposed method is more robust and shows that some syntactic structures prompt more toxic content while others could prompt less biased generation. |
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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
| Challenge: | generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say . |
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