The Power of Scale for Parameter-Efficient Prompt Tuning
In this work, we explore "prompt tuning", a simple yet effective mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signal from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3'
Revisiting Few-sample BERT Fine-tuning
This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network for down-stream tasks; and the prevalent pract
ALBERT: A Lite BERT for Self-supervised Learning of Language...
Published: 20 Dec 2019, Last Modified: 22 Oct 2023ICLR 2020 Conference Blind SubmissionReaders: Everyone Abstract: Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we pr
MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism
Larger language models are dramatically more useful for NLP tasks such as article completion, question answering, and dialog systems. Training the largest neural language model has recently been the best way to advance the state of the art in NLP applications. Two recent papers, BERT and GPT-2, demonstrate the benefits of large scale language modeling. Both papers leverage advances in compute and
XLNet: Generalized Autoregressive Pretraining for Language Understanding
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy. In light of these pros and cons, we p
Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters
The year 2018 marked a turning point for the field of Natural Language Processing, with a series of deep-learning models achieving state-of-the-art results on NLP tasks ranging from question answering to sentiment classification. Most recently, Google’s BERT algorithm has emerged as a sort of “one model to rule them all,” based on its superior performance over a wide variety of tasks. 🕹 Try out a
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https://nlp.stanford.edu/seminar/details/jdevlin.pdf
Cross-lingual Language Model Pretraining