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Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey

📅 Published: June 27, 2023 👤 Bonan Min, Hayley Ross, Elior Sulem et al. 📖 ACM Computing Surveys 📊 1,154 citations
AI-Generated Summary

Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

Key Findings
  • 1 For numerous NLP tasks, approaches leveraging PLMs have achieved advanced performance.
  • 2 The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks.
  • 3 Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training.
Why It Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source:

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Article Details
Source OpenAlex
Category 🤖 Artificial Intelligence
Published Jun 27, 2023
Journal ACM Computing Surveys
DOI 10.1145/3605943
Citations 1,154
Authors Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen