16 citations · 23 across the 9 of their papers we have counts for
4 papers · 1 filter
Accurate and Data-Efficient Toxicity Prediction when Annotators Disagree
Harbani Jaggi, Kashyap Murali, Eve Fleisig +1
When annotators disagree, predicting the labels given by individual annotators can capture nuances overlooked by traditional label aggregation. We introduce three approaches to pre…
Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination
Eve Fleisig, Genevieve Smith, Madeline Bossi +3
We present a large-scale study of linguistic bias exhibited by ChatGPT covering ten dialects of English (Standard American English, Standard British English, and eight widely spoke…
Standard Language Ideology in AI-Generated Language
Genevieve Smith, Eve Fleisig, Ishita Rustagi +1
Large language models (LLMs) generate text that reinforces standard language ideology: a bias towards certain language varieties that are granted more prestige, authority, and legi…
The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
Eve Fleisig, Su Lin Blodgett, Dan Klein +1
Longstanding data labeling practices in machine learning involve collecting and aggregating labels from multiple annotators. But what should we do when annotators disagree? Though…