论文

以下是关于提示工程的最新论文（按发布日期排序）。我们每天更新，新论文不断涌现。我们每周将这些论文的摘要整合到上面的指南中。

综述

• Nature Language Reasoning, A Survey(opens in a new tab) (March 2023)
• Augmented Language Models: a Survey(opens in a new tab) (Feb 2023)
• A Survey for In-context Learning(opens in a new tab) (Dec 2022)
• Towards Reasoning in Large Language Models: A Survey(opens in a new tab) (Dec 2022)
• Reasoning with Language Model Prompting: A Survey(opens in a new tab) (Dec 2022)
• Emergent Abilities of Large Language Models(opens in a new tab) (Jun 2022)
• A Taxonomy of Prompt Modifiers for Text-To-Image Generation(opens in a new tab) (Apr 2022)

• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing(opens in a new tab) (Jul 2021)

  方法

• Self-Refine: Iterative Refinement with Self-Feedback(opens in a new tab) (Mar 2023)

• kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference(opens in a new tab) (Mar 2023)
• Visual-Language Prompt Tuning with Knowledge-guided Context Optimization(opens in a new tab) (Mar 2023)
• Fairness-guided Few-shot Prompting for Large Language Models(opens in a new tab) (Mar 2023)
• Context-faithful Prompting for Large Language Models(opens in a new tab) (Mar 2023)
• Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning(opens in a new tab) (Mar 2023)
• UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation(opens in a new tab) (Mar 2023)
• Model-tuning Via Prompts Makes NLP Models Adversarially Robust(opens in a new tab) (Mar 2023)
• Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer(opens in a new tab) (March 2023)
• CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification(opens in a new tab) (March 2023)
• Larger language models do in-context learning differently(opens in a new tab) (March 2023)
• OpenICL: An Open-Source Framework for In-context Learning(opens in a new tab) (March 2023)
• Dynamic Prompting: A Unified Framework for Prompt Tuning(opens in a new tab) (March 2023)
• Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning(opens in a new tab) (March 2023)
• Effectiveness of Data Augmentation for Prefix Tuning with Limited Data(opens in a new tab) (March 2023)
• Mixture of Soft Prompts for Controllable Data Generation(opens in a new tab) (March 2023)
• Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners(opens in a new tab) (March 2023)
• How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks(opens in a new tab) (March 2023)
• Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT(opens in a new tab) (Feb 2023)
• EvoPrompting: Language Models for Code-Level Neural Architecture Search(opens in a new tab) (Feb 2023)
• In-Context Instruction Learning(opens in a new tab) (Feb 2023)
• Chain of Hindsight Aligns Language Models with Feedback(opens in a new tab) (Feb 2023)
• Language Is Not All You Need: Aligning Perception with Language Models(opens in a new tab) (Feb 2023)
• Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data(opens in a new tab) (Feb 2023)
• Active Prompting with Chain-of-Thought for Large Language Models(opens in a new tab) (Feb 2023)
• More than you’ve asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models(opens in a new tab) (Feb 2023)
• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT(opens in a new tab) (Feb 2023)
• Guiding Large Language Models via Directional Stimulus Prompting(opens in a new tab) (Feb 2023)
• How Does In-Context Learning Help Prompt Tuning?(opens in a new tab) (Feb 2023)
• Scalable Prompt Generation for Semi-supervised Learning with Language Models(opens in a new tab) (Feb 2023)
• Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints(opens in a new tab) (Feb 2023)
• À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting(opens in a new tab) (Feb 2023)
• GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks(opens in a new tab) (Feb 2023)
• The Capacity for Moral Self-Correction in Large Language Models(opens in a new tab) (Feb 2023)
• SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains(opens in a new tab) (Feb 2023)
• Evaluating the Robustness of Discrete Prompts(opens in a new tab) (Feb 2023)
• Compositional Exemplars for In-context Learning(opens in a new tab) (Feb 2023)
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery(opens in a new tab) (Feb 2023)
• Multimodal Chain-of-Thought Reasoning in Language Models(opens in a new tab) (Feb 2023)
• Large Language Models Can Be Easily Distracted by Irrelevant Context(opens in a new tab) (Feb 2023)
• Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models(opens in a new tab) (Feb 2023)
• Progressive Prompts: Continual Learning for Language Models(opens in a new tab) (Jan 2023)
• Batch Prompting: Efficient Inference with LLM APIs(opens in a new tab) (Jan 2023)
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP(opens in a new tab) (Dec 2022)
• On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning(opens in a new tab) (Dec 2022)
• Constitutional AI: Harmlessness from AI Feedback(opens in a new tab) (Dec 2022)
• Successive Prompting for Decomposing Complex Questions(opens in a new tab) (Dec 2022)
• Large Language Models are reasoners with Self-Verification(opens in a new tab) (Dec 2022)
• Discovering Language Model Behaviors with Model-Written Evaluations(opens in a new tab) (Dec 2022)
• Structured Prompting: Scaling In-Context Learning to 1,000 Examples(opens in a new tab) (Dec 2022)
• PAL: Program-aided Language Models(opens in a new tab) (Nov 2022)
• Large Language Models Are Human-Level Prompt Engineers(opens in a new tab) (Nov 2022)
• Ignore Previous Prompt: Attack Techniques For Language Models(opens in a new tab) (Nov 2022)
• Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods(opens in a new tab) (Nov 2022)
• Teaching Algorithmic Reasoning via In-context Learning(opens in a new tab) (Nov 2022)
• Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference(opens in a new tab) (Nov 2022)
• Ask Me Anything: A simple strategy for prompting language models(opens in a new tab) (Oct 2022)
• Recitation-Augmented Language Models(opens in a new tab) (Oct 2022)
• ReAct: Synergizing Reasoning and Acting in Language Models(opens in a new tab) (Oct 2022)
• Prompting GPT-3 To Be Reliable(opens in a new tab) (Oct 2022)
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks(opens in a new tab) (Oct 2022)
• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought(opens in a new tab) (Oct 2022)
• Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples(opens in a new tab) (Sep 2022)
• Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning(opens in a new tab) (Sep 2022)
• Promptagator: Few-shot Dense Retrieval From 8 Examples(opens in a new tab) (Sep 2022)
• Atlas: Few-shot Learning with Retrieval Augmented Language Models(opens in a new tab) (Nov 2022)
• DocPrompting: Generating Code by Retrieving the Docs(opens in a new tab) (July 2022)
• On the Advance of Making Language Models Better Reasoners(opens in a new tab) (June 2022)
• Large Language Models are Zero-Shot Reasoners(opens in a new tab) (May 2022)
• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations(opens in a new tab) (May 2022)
• MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning(opens in a new tab) (May 2022)
• PPT: Pre-trained Prompt Tuning for Few-shot Learning(opens in a new tab) (Mqy 2022)
• Toxicity Detection with Generative Prompt-based Inference(opens in a new tab) (May 2022)
• Learning to Transfer Prompts for Text Generation(opens in a new tab) (May 2022)
• The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning(opens in a new tab) (May 2022)
• A Taxonomy of Prompt Modifiers for Text-To-Image Generation(opens in a new tab) (Apr 2022)
• PromptChainer: Chaining Large Language Model Prompts through Visual Programming(opens in a new tab) (Mar 2022)
• Self-Consistency Improves Chain of Thought Reasoning in Language Models(opens in a new tab) (March 2022)
• Training language models to follow instructions with human feedback(opens in a new tab)
• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?(opens in a new tab) (Feb 2022)
• Chain of Thought Prompting Elicits Reasoning in Large Language Models(opens in a new tab) (Jan 2022)
• Show Your Work: Scratchpads for Intermediate Computation with Language Models(opens in a new tab) (Nov 2021)
• AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts(opens in a new tab) (Oct 2021)
• Generated Knowledge Prompting for Commonsense Reasoning(opens in a new tab) (Oct 2021)
• Multitask Prompted Training Enables Zero-Shot Task Generalization(opens in a new tab) (Oct 2021)
• Reframing Instructional Prompts to GPTk’s Language(opens in a new tab) (Sep 2021)
• Design Guidelines for Prompt Engineering Text-to-Image Generative Models(opens in a new tab) (Sep 2021)
• Making Pre-trained Language Models Better Few-shot Learners(opens in a new tab) (Aug 2021)
• Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity(opens in a new tab) (April 2021)
• BERTese: Learning to Speak to BERT(opens in a new tab) (April 2021)
• The Power of Scale for Parameter-Efficient Prompt Tuning(opens in a new tab) (April 2021)
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm(opens in a new tab) (Feb 2021)
• Calibrate Before Use: Improving Few-Shot Performance of Language Models(opens in a new tab) (Feb 2021)
• Prefix-Tuning: Optimizing Continuous Prompts for Generation(opens in a new tab) (Jan 2021)
• Learning to Generate Task-Specific Adapters from Task Description(opens in a new tab) (Jan 2021)
• Making Pre-trained Language Models Better Few-shot Learners(opens in a new tab) (Dec 2020)
• Learning from Task Descriptions(opens in a new tab) (Nov 2020)
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts(opens in a new tab) (Oct 2020)
• Language Models are Few-Shot Learners(opens in a new tab) (May 2020)
• How Can We Know What Language Models Know?(opens in a new tab) (July 2020)

• Scaling Laws for Neural Language Models(opens in a new tab) (Jan 2020)

  应用

• PaLM 2 Technical Report(opens in a new tab) (May 2023)

• BloombergGPT: A Large Language Model for Finance(opens in a new tab) (March 2023)
• Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts(opens in a new tab) (March 2023)
• Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes(opens in a new tab) (March 2023)
• TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs(opens in a new tab) (March 2023)
• Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning(opens in a new tab) (March 2023)
• Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses(opens in a new tab) (March 2023)
• Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning(opens in a new tab) (March 2023)
• Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation(opens in a new tab) (March 2023)
• Zero-shot Model Diagnosis(opens in a new tab) (March 2023)
• Prompting Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages(opens in a new tab) (March 2023)
• SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization(opens in a new tab) (March 2023)
• Large Language Models and Simple, Stupid Bugs(opens in a new tab) (March 2023)
• Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?(opens in a new tab) (Mar 2023)
• SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models(opens in a new tab) (Mar 2023)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction(opens in a new tab) (March 2023)
• MathPrompter: Mathematical Reasoning using Large Language Models(opens in a new tab) (March 2023)
• Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums(opens in a new tab) (March 2023)
• Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting(opens in a new tab) (March 2023)
• Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering(opens in a new tab) (March 2023)
• Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis(opens in a new tab) (March 2023)
• SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks(opens in a new tab) (March 2023)
• Goal Driven Discovery of Distributional Differences via Language Descriptions(opens in a new tab) (Feb 2023)
• Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models(opens in a new tab) (Feb 2023)
• TabGenie: A Toolkit for Table-to-Text Generation(opens in a new tab) (Feb 2023)
• SGL-PT: A Strong Graph Learner with Graph Prompt Tuning(opens in a new tab) (Feb 2023)
• Few-Shot Table-to-Text Generation with Prompt-based Adapter(opens in a new tab) (Feb 2023)
• Language Models Are Few-shot Learners for Prognostic Prediction(opens in a new tab) (Feb 2023)
• STA: Self-controlled Text Augmentation for Improving Text Classifications(opens in a new tab) (Feb 2023)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback(opens in a new tab) (Feb 2023)
• How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study(opens in a new tab) (Feb 2023)
• Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales(opens in a new tab) (Feb 2023)
• LabelPrompt: Effective Prompt-based Learning for Relation Classification(opens in a new tab) (Feb 2023)
• Language Model Crossover: Variation through Few-Shot Prompting(opens in a new tab) (Feb 2023)
• Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition(opens in a new tab) (Feb 2023)
• The Capacity for Moral Self-Correction in Large Language Models(opens in a new tab) (Feb 2023)
• Prompting for Multimodal Hateful Meme Classification(opens in a new tab) (Feb 2023)
• PLACES: Prompting Language Models for Social Conversation Synthesis(opens in a new tab) (Feb 2023)
• Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation(opens in a new tab) (Feb 2023)
• Crawling the Internal Knowledge-Base of Language Models(opens in a new tab) (Jan 2023)
• Legal Prompt Engineering for Multilingual Legal Judgement Prediction(opens in a new tab) (Dec 2022)
• Investigating Prompt Engineering in Diffusion Models(opens in a new tab) (Nov 2022)
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering(opens in a new tab) (Sep 2022)
• Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language(opens in a new tab) (Oct 2022)
• Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?(opens in a new tab) (Oct 2022)
• Plot Writing From Scratch Pre-Trained Language Models(opens in a new tab) (July 2022)

• Survey of Hallucination in Natural Language Generation(opens in a new tab) (Feb 2022)

  收集

• Chain-of-Thought Papers(opens in a new tab)

• Papers with Code(opens in a new tab)
• Prompt Papers(opens in a new tab)