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Found 3 papers, 1 papers with code
ICL Markup: Structuring In-Context Learning using Soft-Token Tags
Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language.
RelatIF: Identifying Explanatory Training Examples via Relative Influence
In this work, we focus on the use of influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine learning model.
Understanding the Origins of Bias in Word Embeddings
Given a word embedding trained on a corpus, our method identifies how perturbing the corpus will affect the bias of the resulting embedding.
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