Weight-based model editing methods update the parametric knowledge of language models post-training. However, these methods can unintentionally alter unrelated parametric knowledge representations, potentially increasing the risk of harm. In this work, we investigate how weight editing methods unexpectedly amplify model biases after edits. We introduce a novel benchmark dataset, Seesaw-CF, for measuring bias amplification of model editing methods for demographic traits such as race, geographic origin, and gender. We use Seesaw-CF to examine the impact of model editing on bias in five large language models. Our results demonstrate that edited models exhibit, to various degrees, more biased behavior for certain demographic groups than before they were edited, specifically becoming less confident in properties for Asian and African subjects. Additionally, editing facts about place of birth, country of citizenship, or gender has particularly negative effects on the model's knowledge about unrelated properties, such as field of work, a pattern observed across multiple models.

“Flex Tape Can’t Fix That”: Bias and Misinformation in Edited Language Models

Stereotypes are inferences drawn about people based on their demographic attributes, which may result in harms to users when a system is deployed. In generative language-inference tasks, given a premise, a model produces plausible hypotheses that follow either logically (natural language inference) or commonsensically (commonsense inference). Such tasks are therefore a fruitful setting in which to explore the degree to which NLP systems encode stereotypes. In our work, we study how stereotypes manifest when the potential targets of stereotypes are situated in real-life, neutral contexts. We collect human judgments on the presence of stereotypes in generated inferences, and compare how perceptions of stereotypes vary due to annotator positionality.

Analyzing Stereotypes in Generative Text Inference Tasks

Stereotypes are inferences drawn about people based on their demographic attributes, which may result in harms to users when a system is deployed.
In generative language-inference tasks, given a premise, a model produces plausible hypotheses that follow either logically (natural language inference) or common-sensically (commonsense inference).
Such tasks are therefore a fruitful setting in which to explore the degree to which NLP systems encode stereotypes.
In our work, we study how stereotypes manifest when the potential targets of stereotypes are situated in real-life, neutral contexts.
We collect human judgments on the presence of stereotypes in generated inferences and compare how perceptions of stereotypes vary due to annotator positionality.

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Findings Papers - Ethics in NLP

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Anna Sotnikova

3

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Presentations

“Flex Tape Can’t Fix That”: Bias and Misinformation in Edited Language Models

Analyzing Stereotypes in Generative Text Inference Tasks

Analyzing Stereotypes in Generative Text Inference Tasks

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