• Corpus ID: 246036341

Instance-aware Prompt Learning for Language Understanding and Generation

@article{Jin2022InstanceawarePL,
  title={Instance-aware Prompt Learning for Language Understanding and Generation},
  author={Feihu Jin and Jinliang Lu and Jiajun Zhang and Chengqing Zong},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.07126}
}
Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of parameters. The current usage of discrete and continuous prompts assumes that the prompt is fixed for a specific task and all samples in the task share the same prompt. However, a task may contain quite diverse samples in which some are easy and others are difficult, and diverse prompts are desirable. In this paper, we… 

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References

SHOWING 1-10 OF 29 REFERENCES

Making Pre-trained Language Models Better Few-shot Learners

The LM-BFF approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.

Prefix-Tuning: Optimizing Continuous Prompts for Generation

Prefix-tuning is proposed, a lightweight alternative to fine- Tuning for natural language generation tasks, which keeps language model parameters frozen and instead optimizes a sequence of continuous task-specific vectors, which is called the prefix.

The Power of Scale for Parameter-Efficient Prompt Tuning

This work explores “prompt tuning”, a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks, and shows that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, as compared to full model tuning.

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

This work introduces Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task.

RobBERT: a Dutch RoBERTa-based Language Model

It is found that RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets, indicating that it is a powerful pre-trained model for a large variety of Dutch language tasks.

Factual Probing Is [MASK]: Learning vs. Learning to Recall

OptiPrompt is proposed, a novel and efficient method which directly optimizes in continuous embedding space and is able to predict an additional 6.4% of facts in the LAMA benchmark.

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

Improving and Simplifying Pattern Exploiting Training

This paper focuses on few-shot learning without any unlabeled data and introduces ADAPET, which modifies PET’s objective to provide denser supervision during fine-tuning and outperforms PET on SuperGLUE without any task-specific unlabeling data.

DART: Open-Domain Structured Data Record to Text Generation

The dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing.

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

A new benchmark styled after GLUE is presented, a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard are presented.