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Found 3 papers, 1 papers with code
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
This work delves into the expanding role of large language models (LLMs) in generating artificial data.
Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions
In our analysis of the results, we found that the choice of imputation method significantly impacts soft label changes and distribution.
Quirk or Palmer: A Comparative Study of Modal Verb Frameworks with Annotated Datasets
We observe that both frameworks have similar inter-annotator agreements, despite having different numbers of sense types (8 for Quirk and 3 for Palmer).
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