AI RESEARCH
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations
arXiv CS.CL
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ArXi:2510.16458v2 Announce Type: replace Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning behind annotators' decisions. One such approach is the LiTEx taxonomy, which categorizes free-text explanations in English into reasoning categories. However, previous work applying LiTEx has focused on within-label variation: cases where annotators agree on the NLI label but provide different explanations.