ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning

• URL: http://arxiv.org/abs/2211.04118v3
• Date: Tue, 12 Mar 2024 08:29:41 GMT
• Title: ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning
• Authors: Jinta Weng and Yifan Deng and d Donghao Li and Hao You and Yue Hu and Heyan Huang
• Abstract summary: We explore utilizing suitable contrastive samples and multi-degree contrastive learning methods to improve the robustness of the prompt representation. Our results exhibit state-of-the-art performance in different few-shot settings, and the ablation experiments also certify the effectiveness of utilizing multi-degree contrastive learning in the prompt-based fine-tuning process.
• Score: 37.219617741198334
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The prompt has become an effective linguistic tool for utilizing pre-trained language models. However, in few-shot scenarios, subtle changes in the prompt design always make the result widely different, and the prompt learning methods also make it easy to overfit the limited samples. To alleviate this, we explore utilizing suitable contrastive samples and multi-degree contrastive learning methods to improve the robustness of the prompt representation. Therefore, the proposed Consprompt combined with the prompt encoding network, contrastive sampling modules, and contrastive scoring modules, is introduced to realize differential contrastive learning. Our results exhibit state-of-the-art performance in different few-shot settings, and the ablation experiments also certify the effectiveness of utilizing multi-degree contrastive learning in the prompt-based fine-tuning process.

Related papers

• ParaICL: Towards Robust Parallel In-Context Learning [74.38022919598443]
  Large language models (LLMs) have become the norm in natural language processing. Few-shot in-context learning (ICL) relies on the choice of few-shot demonstration examples. We propose a novel method named parallel in-context learning (ParaICL)
  arXiv  Detail & Related papers  (2024-03-31T05:56:15Z)
• KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning [31.139620652818838]
  We propose KDMCSE, a novel approach that enhances the discrimination and generalizability of multimodal representation. We also introduce a new contrastive objective, AdapACSE, that enhances the discriminative representation by strengthening the margin within the angular space.
  arXiv  Detail & Related papers  (2024-03-26T08:32:39Z)
• In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model [13.983810804606264]
  We propose In-Context Prompt Learning (InCPL) for test-time visual recognition tasks. InCPL associates a new test sample with very few labeled examples as context information. We introduce a context-aware unsupervised loss to optimize visual prompts tailored to test samples.
  arXiv  Detail & Related papers  (2024-03-10T08:15:51Z)
• 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)
• CPL: Counterfactual Prompt Learning for Vision and Language Models [76.18024920393245]
  This paper presents a novel underlinetextbfCounterfactual underlinetextbfPrompt underlinetextbfLearning (CPL) method for vision and language models. CPL simultaneously employs counterfactual generation and contrastive learning in a joint optimization framework. Experiments demonstrate that CPL can obtain superior few-shot performance on different vision and language tasks.
  arXiv  Detail & Related papers  (2022-10-19T08:06:39Z)
• Bayesian Prompt Learning for Image-Language Model Generalization [64.50204877434878]
  We use the regularization ability of Bayesian methods to frame prompt learning as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space.
  arXiv  Detail & Related papers  (2022-10-05T17:05:56Z)
• Instance-aware Prompt Learning for Language Understanding and Generation [49.22899822734549]
  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.
  arXiv  Detail & Related papers  (2022-01-18T17:03:25Z)
• Constructing Contrastive samples via Summarization for Text Classification with limited annotations [46.53641181501143]
  We propose a novel approach to constructing contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations with limited annotations. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News) demonstrate the effectiveness of the proposed contrastive learning framework.
  arXiv  Detail & Related papers  (2021-04-11T20:13:24Z)

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.