- Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
- Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
- Language Models are Few-Shot Learners
- Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
- KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
- Text Generation with Efficient (Soft) Q-Learning
- Multitask Prompted Training Enables Zero-shot Task Generalization
- Pre-Trained Models: Past, Present and Future
- Learning To Prompt For Vision-language Models
- Towards A Unified View Of Parameter-efficient Transfer Learning
- Cross-Task Generalization via Natural Language Crowdsourcing Instructions
- WARP: Word-level Adversarial ReProgramming
- GPT Understands, Too
- Prefix-Tuning: Optimizing Continuous Prompts for Generation
- Prompt-Learning for Fine-Grained Entity Typing
- Noisy Channel Language Model Prompting for Few-Shot Text Classification
- Making Pre-trained Language Models Better Few-shot Learners
- Exploring Low-dimensional Intrinsic Task Subspace via Prompt Tuning
- Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
- P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
- Finetuned Language Models Are Zero-shot Learners
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- How Many Data Points is a PromptWorth?
- Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
- Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
- CPT: Colorful Prompt Tuning For Pre-trained Vision-language Models
- Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2
- A Good Prompt Is Worth Millions of Parameters? Low-resource Prompt-based Learning for Vision-Language Models
- CONTROL PREFIXES for Text Generation
- Factual Probing Is [MASK]: Learning vs. Learning to Recall
- True Few-Shot Learning with Language Models
- Paradigm Shift in Natural Language Processing
- SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
- Constrained Language Models Yield Few-Shot Semantic Parsers
- PTR: Prompt Tuning with Rules for Text Classification
- The Power of Prompt Tuning for Low-Resource Semantic Parsing
- Parameter-Efficient Transfer Learning for NLP
- Calibrate Before Use: Improving Few-Shot Performance of Language Models
- NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction
- Do Prompt-Based Models Really Understand the Meaning of their Prompts?
- AUTOPROMPT: Eliciting Knowledge from Language Models with Automatically Generated Prompts
- Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
- Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
- Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
- Revisiting Self-Training for Few-Shot Learning of Language Model
- The Power of Scale for Parameter-Efficient Prompt Tuning
- PPT: Pre-trained Prompt Tuning for Few-shot Learning
- Improving and Simplifying Pattern Exploiting Training
- Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
- GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
- PADA: A Prompt-based Autoregressive Approach for Adaptation to Unseen Domains
- Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
- How Can We Know What Language Models Know?
- It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
- Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
- MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
- Template-free Prompt Tuning for Few-shot NER
- Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
- What Makes Good In-Context Examples for GPT-3?
- Language Models as Knowledge Bases?
- Automatically IdentifyingWords That Can Serve as Labels for Few-Shot Text Classification
- Prompt Tuning or Fine-Tuning - Investigating Relational Knowledge in Pre-Trained Language Models
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
manjunath5496/Prompt-Tuning-Papers
"The more the universe seems comprehensible, the more it also seems pointless."― Steven Weinberg