A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI (2025.findings-acl)
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| Challenge: | Recent research has shown that explanations provide valuable information for understanding human label variation (HLV) Large language models (LLMs) can approximate HJD from a few human-provided label-explanation pairs, but collecting explanations for every label is still time-consuming. |
| Approach: | They propose to use Large Language Models (LLMs) as annotators to generate model explanations for a few given human labels. |
| Outcome: | The proposed models can generate human-provided explanations from human labels, but they are still time-consuming. |
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Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
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