Papers with approximating HJD
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. |