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Language models (LMs) have been shown to memorize a great deal of factual knowledge contained in their training data.
Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite. 2020 · 1909
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Sticking to the facts: Confident decoding for faithful data-to-text generation
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The influence curve and its role in robust estimation
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Okapi at trec-3
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How much knowledge can you pack into the parameters of a language model?
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K Simonyan, A Vedaldi, and A Zisserman. 2014 · 2014
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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FastIF: Scalable influence functions for efficient model interpretation and debugging
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Explaining black box predictions and unveiling data artifacts through influence functions
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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First is better than last for training data influence
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