5 citations · 9 across the 5 of their papers we have counts for
6 papers
Monte Carlo Sampling for Analyzing In-Context Examples
Stephanie Schoch, Yangfeng Ji
Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on s…
In-Context Learning (and Unlearning) of Length Biases
Stephanie Schoch, Yangfeng Ji
Large language models have demonstrated strong capabilities to learn in-context, where exemplar input-output pairings are appended to the prompt for demonstration. However, existin…
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values
Stephanie Schoch, Ritwick Mishra, Yangfeng Ji
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…
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Stephanie Schoch, Haifeng Xu, Yangfeng Ji
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label det…
Contextualizing Variation in Text Style Transfer Datasets
Stephanie Schoch, Wanyu Du, Yangfeng Ji
Text style transfer involves rewriting the content of a source sentence in a target style. Despite there being a number of style tasks with available data, there has been limited s…
Underreporting of errors in NLG output, and what to do about it
Emiel van Miltenburg, Miruna-Adriana Clinciu, Ondřej Dušek +8
We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make. This is a problem, because mistakes are an important indicator o…