331 citations · 332 across the 5 of their papers we have counts for
9 papers · 1 filter
Humans and LLMs Diverge on Probabilistic Inferences
Gaurav Kamath, Sreenath Madathil, Sebastian Schuster +2
Human reasoning often involves working over limited information to arrive at probabilistic conclusions. In its simplest form, this involves making an inference that is not strictly…
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations
Pingjun Hong, Beiduo Chen, Siyao Peng +3
Natural Language Inference (NLI) datasets often exhibit human label variation. To better understand these variations, explanation-based approaches analyze the underlying reasoning…
A survey of diversity quantification in natural language processing: The why, what, where and how
Louis Estève, Marie-Catherine de Marneffe, Nurit Melnik +2
The concept of diversity has received increasing attention in natural language processing (NLP) in recent years. It became an advocated property of datasets and systems, and many m…
LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference
Pingjun Hong, Beiduo Chen, Siyao Peng +2
There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. Howev…
Explanation sensitivity to the randomness of large language models: the case of journalistic text classification
Jeremie Bogaert, Marie-Catherine de Marneffe, Antonin Descampe +3
Large language models (LLMs) perform very well in several natural language processing tasks but raise explainability challenges. In this paper, we examine the effect of random elem…
Investigating Reasons for Disagreement in Natural Language Inference
Nan-Jiang Jiang, Marie-Catherine de Marneffe
We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 high-level clas…