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

• URL: http://arxiv.org/abs/2111.01243v1
• Date: Mon, 1 Nov 2021 20:08:05 GMT
• Title: Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
• Authors: Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heinz, and Dan Roth
• Abstract summary: Large, pre-trained language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches.
• Score: 67.82942975834924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. We also present approaches that use pre-trained language models to generate data for training augmentation or other purposes. We conclude with discussions on limitations and suggested directions for future research.

Related papers

• Bridging the Gap Between Training and Inference of Bayesian Controllable Language Models [58.990214815032495]
  Large-scale pre-trained language models have achieved great success on natural language generation tasks. BCLMs have been shown to be efficient in controllable language generation. We propose a "Gemini Discriminator" for controllable language generation which alleviates the mismatch problem with a small computational cost.
  arXiv  Detail & Related papers  (2022-06-11T12:52:32Z)
• bert2BERT: Towards Reusable Pretrained Language Models [51.078081486422896]
  We propose bert2BERT, which can effectively transfer the knowledge of an existing smaller pre-trained model to a large model. bert2BERT saves about 45% and 47% computational cost of pre-training BERT_BASE and GPT_BASE by reusing the models of almost their half sizes.
  arXiv  Detail & Related papers  (2021-10-14T04:05:25Z)
• A Comprehensive Comparison of Pre-training Language Models [0.5139874302398955]
  We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results show that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for short text understanding.
  arXiv  Detail & Related papers  (2021-06-22T02:12:29Z)
• HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish [4.473327661758546]
  This paper presents the first ablation study focused on Polish, which, unlike the isolating English language, is a fusional language. We design and thoroughly evaluate a pretraining procedure of transferring knowledge from multilingual to monolingual BERT-based models. Based on the proposed procedure, a Polish BERT-based language model -- HerBERT -- is trained.
  arXiv  Detail & Related papers  (2021-05-04T20:16:17Z)
• Pre-Training a Language Model Without Human Language [74.11825654535895]
  We study how the intrinsic nature of pre-training data contributes to the fine-tuned downstream performance. We find that models pre-trained on unstructured data beat those trained directly from scratch on downstream tasks. To our great astonishment, we uncover that pre-training on certain non-human language data gives GLUE performance close to performance pre-trained on another non-English language.
  arXiv  Detail & Related papers  (2020-12-22T13:38:06Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)
• Pre-training Polish Transformer-based Language Models at Scale [1.0312968200748118]
  We present two language models for Polish based on the popular BERT architecture. We describe our methodology for collecting the data, preparing the corpus, and pre-training the model. We then evaluate our models on thirteen Polish linguistic tasks, and demonstrate improvements in eleven of them.
  arXiv  Detail & Related papers  (2020-06-07T18:48:58Z)
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning [74.25168207651376]
  Fine-tuning pre-trained language models to downstream cross-lingual tasks has shown promising results. We leverage continual learning to preserve the cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. Our methods achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
  arXiv  Detail & Related papers  (2020-04-29T14:07:18Z)
• From English To Foreign Languages: Transferring Pre-trained Language Models [0.12691047660244334]
  Pre-trained models have demonstrated their effectiveness in many downstream natural language processing (NLP) tasks. The availability of multilingual pre-trained models enables zero-shot transfer of NLP tasks from high resource languages to low resource ones. We tackle the problem of transferring an existing pre-trained model from English to other languages under a limited computational budget.
  arXiv  Detail & Related papers  (2020-02-18T00:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.