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Found 5 papers, 2 papers with code
“This is a Problem, Don’t You Agree?” Framing and Bias in Human Evaluation for Natural Language Generation
Despite recent efforts reviewing current human evaluation practices for natural language generation (NLG) research, the lack of reported question wording and potential for framing effects or cognitive biases influencing results has been widely overlooked.
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values
Although Shapley values have been shown to be highly effective for identifying harmful training instances, dataset size and model complexity constraints limit the ability to apply Shapley-based data valuation to fine-tuning large pre-trained language models.
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Our theoretical analysis shows the proposed value function is (essentially) the unique function that satisfies two desirable properties for evaluating data values in classification.
Contextualizing Variation in Text Style Transfer Datasets
Text style transfer involves rewriting the content of a source sentence in a target style.
Underreporting of errors in NLG output, and what to do about it
We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make.
