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Human label variation (Plank 2022), or annotation disagreement, exists inmany natural language processing (NLP) tasks. To be robust and trusted, NLPmodels need to identify such variation and be able to explain it. To this end,we created the first ecologically valid explanation dataset with diversereasoning, LiveNLI. LiveNLI contains annotators' highlights and free-textexplanations for the label(s) of their choice for 122 English Natural LanguageInference items, each with at least 10 annotations. We used its explanationsfor chain-of-thought prompting, and found there is still room for improvementin GPT-3's ability to predict label distribution with in-context learning.

Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation