转自与知乎:刘聪NLP

本文整理了NAACL2022中Prompt相关的论文，并把论文中对应的Github链接也附上了。

官方论文列表：https://2022.naacl.org/program/accepted_papers/

Prompt相关论文

[1] *Template-free Prompt Tuning for Few-shot NER

Github: https://github.com/rtmaww/EntLM/

[2]IDPG: An Instance-Dependent Prompt Generation Method

[3]Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

Github: https://github.com/HanNight/AMuLaP

[4]ProQA: Structural Prompt-based Pre-training for Unified Question Answering

[5]On Transferability of Prompt Tuning for Natural Language Processing

Github: https://github.com/thunlp/Prompt-Transferability

[6]PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts

Github: https://github.com/Alrope123/prompt-waywardness

[7]Learning to Transfer Prompts for Text Generation

Github: https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation

[8]*Learning To Retrieve Prompts for In-Context Learning

Github: https://github.com/OhadRubin/EPR

[9]Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Github: https://github.com/awebson/prompt_semantics

[10]Go Back in Time: Generating Flashbacks in Stories with Event Plots and Temporal Prompts

Github: https://github.com/PlusLabNLP/flashback_gen

[11]Probing via Prompting and Pruning

[12]Contrastive Learning for Prompt-based Few-shot Language Learners

Github: https://github.com/yiren-jian/LM-SupCon

[13]Using Natural Sentence Prompts for Understanding Biases in Language Models

[14]Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction

[15]LiST: Lite Prompted Self-training Makes Efficient Few-shot Learners

Github: https://github.com/microsoft/LiST

[16]Exploring the Universal Vulnerability of Prompt-based Learning Paradigm

Github: https://github.com/leix28/prompt-universal-vulnerability

[17]Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection

[18]RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning

[19]On Measuring Social Biases in Prompt-Based Learning

[20]Few-Shot Self-Rationalization with Natural Language Prompts

Github: https://github.com/allenai/feb

[21]SEQZERO: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models

Github: https://github.com/amzn/SeqZero

[22]PromptGen: Automatically Generate Prompts using Generative Models

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