text: 112,150,1316911104279314432,16677638,Alex Beutel,"New research from a collaboration between the Language Research team and my team on gendered correlations in language models (led by Kellie Webster, Xuezhi Wang, and @iftenney)! Paper: <LINK>  A few exciting results in addition to those in the blog post (1/): <LINK> The blog post discusses how applying different language models to coreference resolution can give nearly the same accuracy but make assumptions based on gender at very different rates. We see this not just for coref, but also 3 other tasks (including 2 new ones). (2/) From the blog post but worth highlighting again: these significant differences in the usage of gendered correlations can come from *seemingly innocuous* configuration changes, like changing dropout. Among many take-aways, test thoroughly! (3/) <LINK> We find counterfactual data augmentation (CDA) also can help decrease usage of gendered correlations. Particularly (academically) interesting, we find that CDA generalizes---applying CDA for some names decreases making gendered assumptions for other names! (4/) Further, both of these *pre-training* changes can improve gendered correlations in downstream tasks---we can see the benefits maintained over the course of fine-tuning. (5/) <LINK> Based on this research we discuss some best practices for language modeling. And more generally I'm excited that this research suggests there is a path for broad benefits in this space from improving underlying pre-trained language models. (/end)",https://arxiv.org/abs/2010.06032,"Pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode artifacts undesired in many applications, such as professions correlating with one gender more than another. We explore such gendered correlations as a case study for how to address unintended correlations in pre-trained models. We define metrics and reveal that it is possible for models with similar accuracy to encode correlations at very different rates. We show how measured correlations can be reduced with general-purpose techniques, and highlight the trade offs different strategies have. With these results, we make recommendations for training robust models: (1) carefully evaluate unintended correlations, (2) be mindful of seemingly innocuous configuration differences, and (3) focus on general mitigations. ",Measuring and Reducing Gendered Correlations in Pre-trained Models,6,"['New research from a collaboration between the Language Research team and my team on gendered correlations in language models (led by Kellie Webster, Xuezhi Wang, and @iftenney)!\n\nPaper: <LINK> \n\nA few exciting results in addition to those in the blog post (1/): <LINK>', 'The blog post discusses how applying different language models to coreference resolution can give nearly the same accuracy but make assumptions based on gender at very different rates.  We see this not just for coref, but also 3 other tasks (including 2 new ones). (2/)', 'From the blog post but worth highlighting again: these significant differences in the usage of gendered correlations can come from *seemingly innocuous* configuration changes, like changing dropout. Among many take-aways, test thoroughly! (3/) https://t.co/PG93ascGHG', 'We find counterfactual data augmentation (CDA) also can help decrease usage of gendered correlations. Particularly (academically) interesting, we find that CDA generalizes---applying CDA for some names decreases making gendered assumptions for other names! (4/)', 'Further, both of these *pre-training* changes can improve gendered correlations in downstream tasks---we can see the benefits maintained over the course of fine-tuning. (5/) https://t.co/HDt4JS5eyC', ""Based on this research we discuss some best practices for language modeling. And more generally I'm excited that this research suggests there is a path for broad benefits in this space from improving underlying pre-trained language models. (/end)""]",20,10,1476