Instance-aware Prompt Learning for Language Understanding and Generation

• URL: http://arxiv.org/abs/2201.07126v1
• Date: Tue, 18 Jan 2022 17:03:25 GMT
• Title: Instance-aware Prompt Learning for Language Understanding and Generation
• Authors: Feihu Jin, Jinliang Lu, Jiajun Zhang and Chengqing Zong
• Abstract summary: We propose an instance-aware prompt learning method that learns a different prompt for each instance. Our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.
• Score: 49.22899822734549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method obtains considerable improvements compared to strong baselines. Especially, our method achieves the state-of-the-art on the SuperGLUE few-shot learning benchmark.

Related papers

• Multi-Prompt with Depth Partitioned Cross-Modal Learning [25.239388488952375]
  Partitioned Multi-modal Prompt (PMPO) is a multi-modal prompting technique that extends the soft prompt from a single learnable prompt to multiple prompts. Our method divides the visual encoder depths and connects learnable prompts to the separated visual depths, enabling different prompts to capture hierarchical contextual depths. We evaluate the effectiveness of our approach on three challenging tasks: new class generalization, cross-dataset evaluation, and domain generalization.
  arXiv  Detail & Related papers  (2023-05-10T14:54:29Z)
• Demystifying Prompts in Language Models via Perplexity Estimation [109.59105230163041]
  Performance of a prompt is coupled with the extent to which the model is familiar with the language it contains. We show that the lower the perplexity of the prompt is, the better the prompt is able to perform the task.
  arXiv  Detail & Related papers  (2022-12-08T02:21:47Z)
• TEMPERA: Test-Time Prompting via Reinforcement Learning [57.48657629588436]
  We propose Test-time Prompt Editing using Reinforcement learning (TEMPERA) In contrast to prior prompt generation methods, TEMPERA can efficiently leverage prior knowledge. Our method achieves 5.33x on average improvement in sample efficiency when compared to the traditional fine-tuning methods.
  arXiv  Detail & Related papers  (2022-11-21T22:38:20Z)
• Instance-wise Prompt Tuning for Pretrained Language Models [72.74916121511662]
  Instance-wise Prompt Tuning (IPT) is the first prompt learning paradigm that injects knowledge from the input data instances to the prompts. IPT significantly outperforms task-based prompt learning methods, and achieves comparable performance to conventional finetuning with only 0.5% - 1.5% of tuned parameters.
  arXiv  Detail & Related papers  (2022-06-04T10:08:50Z)
• IDPG: An Instance-Dependent Prompt Generation Method [58.45110542003139]
  Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage. We propose a conditional prompt generation method to generate prompts for each input instance.
  arXiv  Detail & Related papers  (2022-04-09T15:45:27Z)
• Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning [41.15017636192417]
  We present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning Language Models. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-01T02:24:24Z)
• CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems [56.302581679816775]
  This paper proposes Comprehensive Instruction (CINS) that exploits PLMs with task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD. Experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data.
  arXiv  Detail & Related papers  (2021-09-10T03:23:06Z)
• Reordering Examples Helps during Priming-based Few-Shot Learning [6.579039107070663]
  We show that PERO can learn to generalize efficiently using as few as 10 examples. We demonstrate the effectiveness of the proposed method on the tasks of sentiment classification, natural language inference and fact retrieval.
  arXiv  Detail & Related papers  (2021-06-03T11:02:36Z)

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.