We propose a new strategy for applying large pre-trained language models to novel tasks
when labeled training data is limited. Rather than apply the model in a typical zero-shot or …

Large language models (LLMs) are trained on web-scale corpora that inevitably include
contradictory factual information from sources of varying reliability. In this paper, we propose …

Approaching Speech-to-Text and Automatic Speech Recognition problems in low-resource
languages is notoriously challenging due to the scarcity of validated datasets and the …

Prompted weak supervision (PromptedWS) applies pre-trained large language models
(LLMs) as the basis for labeling functions (LFs) in a weak supervision framework to obtain …

Dependency parsing is an essential task in NLP, and the quality of dependency parsers is
crucial for many downstream tasks. Parsers' quality often varies depending on the domain …

Text classification with extremely weak supervision (EWS) imposes stricter supervision
constraints compared to regular weakly supervise classification. Absolutely no labeled …

We introduce RACH-Space, an algorithm for labelling unlabelled data in weakly supervised
learning, given incomplete, noisy information about the labels. RACH-Space offers simplicity …

We study programmatic weak supervision, where in contrast to labeled data, we have
access to\emph {weak labelers}, each of which either abstains or provides noisy labels …

Recent adaptations of machine learning models in many businesses has underscored the
need for quality training data. Typically, training supervised machine learning systems …

In recent years, machine learning, particularly deep learning models, have seen significant
growth and made impact in various real-world applications. These models bypass the need …