Deep transfer learning & beyond: Transformer language models in information systems research

R Gruetzemacher, D Paradice - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
AI is widely thought to be poised to transform business, yet current perceptions of the scope
of this transformation may be myopic. Recent progress in natural language processing …

A holistic approach to undesired content detection in the real world

T Markov, C Zhang, S Agarwal, FE Nekoul… - Proceedings of the …, 2023 - ojs.aaai.org
We present a holistic approach to building a robust and useful natural language
classification system for real-world content moderation. The success of such a system relies …

Finetuned language models are zero-shot learners

J Wei, M Bosma, VY Zhao, K Guu, AW Yu… - arxiv preprint arxiv …, 2021 - arxiv.org
This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

Generating training data with language models: Towards zero-shot language understanding

Y Meng, J Huang, Y Zhang… - Advances in Neural …, 2022 - proceedings.neurips.cc
Pretrained language models (PLMs) have demonstrated remarkable performance in various
natural language processing tasks: Unidirectional PLMs (eg, GPT) are well known for their …

Leveraging large language models in conversational recommender systems

L Friedman, S Ahuja, D Allen, Z Tan… - arxiv preprint arxiv …, 2023 - arxiv.org
A Conversational Recommender System (CRS) offers increased transparency and control to
users by enabling them to engage with the system through a real-time multi-turn dialogue …