Pretrained Language Representations for Text Understanding: A Weakly-Supervised Perspective

Language representations pretrained on general-domain corpora and adapted to downstream task data have achieved enormous success in building natural language understanding (NLU) systems. While the standard supervised fine-tuning of pretrained language models (PLMs) has proven an effective approach for superior NLU performance, it often necessitates a large quantity of costly human-annotated training data. For example, the enormous success of ChatGPT and GPT-4 can be largely credited to their supervised fine-tuning with massive manually-labeled prompt-response training pairs. Unfortunately, obtaining large-scale human annotations is in general infeasible for most practitioners. To broaden the applicability of PLMs to various tasks and settings, weakly-supervised learning offers a promising direction to minimize the annotation requirements for PLM adaptions.