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        <title>Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting</title>
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    <abstract>We advocate the importance of exposing uncertainty on results of language model prompting which display bias modes resembling cognitive biases, and propose to help users grasp the level of uncertainty via simple quantifying metrics. Cognitive biases in the human decision making process can lead to flawed responses when we are under uncertainty. Not surprisingly, we have seen biases in language models resembling cognitive biases as a result of training on biased textual data, raising dangers in downstream tasks that are centered around people’s lives if users trust their results too much. In this work, we reveal two bias modes leveraging cognitive biases when we prompt BERT, accompanied by two bias metrics. On a drug-drug interaction extraction task, our bias measurements reveal an error pattern similar to the availability bias when the labels for training prompts are imbalanced, and show that a toning-down transformation of the drug-drug description in a prompt can elicit a bias similar to the framing effect, warning users to distrust when prompting language models for answers.</abstract>
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%0 Conference Proceedings
%T Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting
%A Lin, Ruixi
%A Ng, Hwee Tou
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lin-ng-2023-mind
%X We advocate the importance of exposing uncertainty on results of language model prompting which display bias modes resembling cognitive biases, and propose to help users grasp the level of uncertainty via simple quantifying metrics. Cognitive biases in the human decision making process can lead to flawed responses when we are under uncertainty. Not surprisingly, we have seen biases in language models resembling cognitive biases as a result of training on biased textual data, raising dangers in downstream tasks that are centered around people’s lives if users trust their results too much. In this work, we reveal two bias modes leveraging cognitive biases when we prompt BERT, accompanied by two bias metrics. On a drug-drug interaction extraction task, our bias measurements reveal an error pattern similar to the availability bias when the labels for training prompts are imbalanced, and show that a toning-down transformation of the drug-drug description in a prompt can elicit a bias similar to the framing effect, warning users to distrust when prompting language models for answers.
%R 10.18653/v1/2023.findings-acl.324
%U https://aclanthology.org/2023.findings-acl.324
%U https://doi.org/10.18653/v1/2023.findings-acl.324
%P 5269-5281

Markdown (Informal)

[Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting](https://aclanthology.org/2023.findings-acl.324) (Lin & Ng, Findings 2023)

• Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting (Lin & Ng, Findings 2023)

ACL

• Ruixi Lin and Hwee Tou Ng. 2023. Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5269–5281, Toronto, Canada. Association for Computational Linguistics.