# PromptKG **Repository Path**: ZJU_NLP/PromptKG ## Basic Information - **Project Name**: PromptKG - **Description**: Prompt Learning-related research works and toolkits for PLM-based Knowledge Graph Embedding Learning, Editing and Applications. - **Primary Language**: Python - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 1 - **Forks**: 0 - **Created**: 2022-12-07 - **Last Updated**: 2023-08-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
**PromptKG Family: a Gallery of Prompt Learning & KG-related research works, toolkits, and paper-list.** [![Awesome](https://awesome.re/badge.svg)](https://github.com/zjunlp/Prompt4ReasoningPapers) [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT) ![](https://img.shields.io/github/last-commit/zjunlp/PromptKG?color=green) ![](https://img.shields.io/badge/PRs-Welcome-red)
| Directory | Description | |-----------|-------------| | [research](research) | • A collection of prompt learning-related **research model implementations** | | [lambdaKG](lambdaKG) | • A library for **PLM-based KG embeddings and applications** | | [deltaKG](deltaKG) | • A library for **dynamically editing PLM-based KG embeddings** | | [tutorial-notebooks](tutorial-notebooks) | • **Tutorial notebooks** for beginners | # Table of Contents * [Tutorials](#Tutorials) * [Surveys](#Surveys) * [Papers](#Papers) * [Knowledge as Prompt](#Knowledge-as-Prompt) * [1. Language Understanding](#Knowledge-as-Prompt) * [2. Multimodal](#Knowledge-as-Prompt) * [3. Advanced Tasks](#Knowledge-as-Prompt) * [Prompt (PLMs) for Knowledge](#prompt-plms-for-knowledge) * [1. Knowledge Probing](#prompt-plms-for-knowledge) * [2. Knowledge Graph Embedding](#prompt-plms-for-knowledge) * [3. Analysis](#prompt-plms-for-knowledge) * [Contact Information](#Contact-Information) # Tutorials - Zero- and Few-Shot NLP with Pretrained Language Models. AACL 2022 Tutorial \[[ppt](https://github.com/allenai/acl2022-zerofewshot-tutorial)\] - Data-Efficient Knowledge Graph Construction. CCKS2022 Tutorial \[[ppt](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\] - Efficient and Robuts Knowledge Graph Construction. AACL-IJCNLP Tutorial \[[ppt](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\] - Knowledge Informed Prompt Learning. MLNLP 2022 Tutorial (Chinese) \[[ppt](https://person.zju.edu.cn/person/attachments/2022-11/01-1668830598-859129.pdf)\] # Surveys * Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models (on arxiv 2021) \[[paper](https://arxiv.org/abs/2203.06904)\] * Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (ACM Computing Surveys 2021) \[[paper](https://arxiv.org/abs/2107.13586)\] * reStructured Pre-training (on arxiv 2022) \[[paper](https://arxiv.org/abs/2206.11147)\] * A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models (on arxiv 2022) \[[paper](https://arxiv.org/abs/2202.08772)\] * A Survey of Knowledge-Enhanced Pre-trained Language Models (on arxiv 2022) \[[paper](https://arxiv.org/abs/2211.05994)\] * A Review on Language Models as Knowledge Bases (on arxiv 2022) \[[paper](https://arxiv.org/abs/2204.06031)\] * Generative Knowledge Graph Construction: A Review (EMNLP, 2022) \[[paper](https://arxiv.org/abs/2210.12714)\] * Reasoning with Language Model Prompting: A Survey (on arxiv 2022) \[[paper](https://arxiv.org/abs/2212.09597)\] * Reasoning over Different Types of Knowledge Graphs: Static, Temporal and Multi-Modal (on arxiv 2022) \[[paper](https://arxiv.org/abs/2212.05767)\] * The Life Cycle of Knowledge in Big Language Models: A Survey (on arxiv 2022) \[[paper](https://arxiv.org/abs/2303.07616)\] # Papers ## Knowledge as Prompt *Language Understanding* - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, in NeurIPS 2020. [\[pdf\]](https://arxiv.org/abs/2005.11401) - REALM: Retrieval-Augmented Language Model Pre-Training, in ICML 2020. [\[pdf\]](https://arxiv.org/abs/2002.08909) - Making Pre-trained Language Models Better Few-shot Learners, in ACL 2022. [\[pdf\]](https://aclanthology.org/2021.acl-long.295/) - PTR: Prompt Tuning with Rules for Text Classification, in OpenAI 2022. [\[pdf\]](https://arxiv.org/pdf/2105.11259.pdf) - Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction, in EMNLP 2021. [\[pdf\]](https://aclanthology.org/2021.emnlp-main.92.pdf) - RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction, in EMNLP 2022 (Findings). [\[pdf\]](https://arxiv.org/abs/2203.09101) - Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2108.02035) - PPT: Pre-trained Prompt Tuning for Few-shot Learning, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2109.04332) - Contrastive Demonstration Tuning for Pre-trained Language Models, in EMNLP 2022 (Findings). [\[pdf\]](https://arxiv.org/abs/2204.04392) - AdaPrompt: Adaptive Model Training for Prompt-based NLP, in arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2202.04824) - KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction, in WWW 2022. [\[pdf\]](https://arxiv.org/abs/2104.07650) - Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning, in NeurIPS 2022. [\[pdf\]](https://arxiv.org/abs/2205.14704) - Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning, in SIGIR 2022. [\[pdf\]](https://arxiv.org/abs/2205.02355) - LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting, in COLING 2022. [\[pdf\]](https://aclanthology.org/2022.coling-1.209/) - Unified Structure Generation for Universal Information Extraction, in ACL 2022. [\[pdf\]](https://aclanthology.org/2022.acl-long.395/) - LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model, in NeurIPS 2022. [\[pdf\]](https://openreview.net/forum?id=a8qX5RG36jd) - Atlas: Few-shot Learning with Retrieval Augmented Language Models, in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2208.03299) - Don't Prompt, Search! Mining-based Zero-Shot Learning with Language Models, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2210.14803) - Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding, in EMNLP 2022. [\[pdf\]](https://arxiv.org/abs/2210.08536) - Unified Knowledge Prompt Pre-training for Customer Service Dialogues, in CIKM 2022. [\[pdf\]](https://arxiv.org/abs/2208.14652) - Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding, in EMNLP 2022. [\[pdf\]](https://arxiv.org/pdf/2210.08536.pdf) - SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions, in arxiv 2022. [\[pdf\]](https://arxiv.org/pdf/2212.10560.pdf) - One Embedder, Any Task: Instruction-Finetuned Text Embeddings, in arxiv 2022. [\[pdf\]](https://arxiv.org/pdf/2212.09741.pdf) - Learning To Retrieve Prompts for In-Context Learning, in NAACL 2022. [\[pdf\]](https://aclanthology.org/2022.naacl-main.191.pdf) - Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data, in ACL 2022. [\[pdf\]](https://aclanthology.org/2022.acl-long.226.pdf) - One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER, in Arxiv 2023. [\[pdf\]](https://arxiv.org/pdf/2301.10410.pdf) - REPLUG: Retrieval-Augmented Black-Box Language Models, in Arxiv 2023. [\[pdf\]](https://arxiv.org/pdf/2301.12652.pdf) *Multimodal* - Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction, in NAACL 2022 (Findings). [\[pdf\]](https://arxiv.org/pdf/2205.03521.pdf) - Visual Prompt Tuning, in ECCV 2022. [\[pdf\]](https://arxiv.org/abs/2203.12119) - CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models, in EMNLP 2022. [\[pdf\]](https://arxiv.org/abs/2109.11797) - Learning to Prompt for Vision-Language Models, in IJCV 2022. [\[pdf\]](https://arxiv.org/abs/2109.01134) - Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models, in NeurIPS 2022. [\[pdf\]](https://arxiv.org/abs/2209.07511) *Advanced Tasks* - Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5), in ACM RecSys 2022. [\[pdf\]](https://arxiv.org/abs/2203.13366) - Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning, in KDD 2022. [\[pdf\]](https://arxiv.org/abs/2206.09363) - PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching, in VLDB 2023. [\[pdf\]](https://arxiv.org/abs/2207.04802) - VIMA: General Robot Manipulation with Multimodal Prompts, in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2210.03094) - Unbiasing Retrosynthesis Language Models with Disconnection Prompts, in Arxiv 2022. [\[pdf\]](https://chemrxiv.org/engage/chemrxiv/article-details/6328d0b8ba8a6d04fc551df7) - ProgPrompt: Generating Situated Robot Task Plans using Large Language Models, in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2209.11302) - Collaborating with language models for embodied reasoning, in NeurIPS 2022 Workshop LaReL. [\[pdf\]](https://openreview.net/pdf?id=YoS-abmWjJc) ## Prompt (PLMs) for Knowledge *Knowledge Probing* - How Much Knowledge Can You Pack Into the Parameters of a Language Model? in EMNLP 2020. [\[pdf\]](https://aclanthology.org/2020.emnlp-main.437/) - Language Models as Knowledge Bases? in EMNLP 2019. [\[pdf\]](https://aclanthology.org/D19-1250.pdf) - Materialized Knowledge Bases from Commonsense Transformers, in CSRR 2022. [\[pdf\]](https://aclanthology.org/2022.csrr-1.5/) - Time-Aware Language Models as Temporal Knowledge Bases, in TACL2022. [\[pdf\]](https://aclanthology.org/2022.tacl-1.15/) - Can Generative Pre-trained Language Models Serve as Knowledge Bases for Closed-book QA? in ACL2021. [\[pdf\]](https://aclanthology.org/2021.acl-long.251/) - Language models as knowledge bases: On entity representations, storage capacity, and paraphrased queries, in EACL2021. [\[pdf\]](https://aclanthology.org/2021.eacl-main.153/) - Scientific language models for biomedical knowledge base completion: an empirical study, in AKBC 2021. [\[pdf\]](https://arxiv.org/abs/2106.09700) - Multilingual LAMA: Investigating knowledge in multilingual pretrained language models, in EACL2021. [\[pdf\]](https://aclanthology.org/2021.eacl-main.284/) - How Can We Know What Language Models Know ? in TACL 2020. [\[pdf\]](https://aclanthology.org/2020.tacl-1.28/) - How Context Affects Language Models' Factual Predictions, in AKBC 2020. [\[pdf\]](https://arxiv.org/abs/2005.04611) - COPEN: Probing Conceptual Knowledge in Pre-trained Language Models, in EMNLP 2022. [\[pdf\]](https://arxiv.org/abs/2211.04079) - Probing Simile Knowledge from Pre-trained Language Models, in ACL 2022. [\[pdf\]](https://aclanthology.org/2022.acl-long.404.pdf) *Knowledge Graph Embedding (We provide a library and benchmark [lambdaKG](https://github.com/zjunlp/PromptKG/tree/main/lambdaKG))* - KG-BERT: BERT for knowledge graph completion, in Arxiv 2020. [\[pdf\]](https://arxiv.org/abs/1909.03193) - Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models， in Coling 2020. [\[pdf\]](https://aclanthology.org/2020.coling-main.153.pdf) - Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion, in WWW 2021. [\[pdf\]](https://arxiv.org/abs/2004.14781) - KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation, TACL 2021 [\[pdf\]](https://arxiv.org/abs/1911.06136) - StATIK: Structure and Text for Inductive Knowledge Graph, in NAACL 2022. [\[pdf\]](https://aclanthology.org/2022.findings-naacl.46/) - Joint Language Semantic and Structure Embedding for Knowledge Graph Completion, in COLING. [\[pdf\]](https://aclanthology.org/2022.coling-1.171/) - Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion, in COLING. [\[pdf\]](https://aclanthology.org/2022.coling-1.352/) - Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach, in ACL 2022. [\[pdf\]](https://aclanthology.org/2022.findings-acl.282/) - Language Models as Knowledge Embeddings, in IJCAI 2022. [\[pdf\]](https://arxiv.org/abs/2206.12617) - From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer, in WWW 2022. [\[pdf\]](https://arxiv.org/abs/2202.02113) - Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings, in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2201.05575) - SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2203.02167) - Sequence to Sequence Knowledge Graph Completion and Question Answering, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2203.10321) - LP-BERT: Multi-task Pre-training Knowledge Graph BERT for Link Prediction, in Arxiv 2022. [\[pdf\]](https://arxiv.org/pdf/2201.04843.pdf) - Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries, in KDD 2022. [\[pdf\]](https://arxiv.org/pdf/2208.07638.pdf) - Knowledge Is Flat: A Seq2Seq Generative framework For Various Knowledge Graph Completion, in Coling 2022. [\[pdf\]](https://arxiv.org/pdf/2209.07299.pdf) *Analysis* - Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases, in ACL 2021. [\[pdf\]](https://aclanthology.org/2021.acl-long.146/) - Can Prompt Probe Pretrained Language Models? Understanding the Invisible Risks from a Causal View, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2203.12258) - How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis, in ACl 2022. [\[pdf\]](https://arxiv.org/abs/2203.16747) - Emergent Abilities of Large Language Models, in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2206.07682) - Knowledge Neurons in Pretrained Transformers, in ACL 2022. [\[pdf\]](https://arxiv.org/abs/2104.08696) - Finding Skill Neurons in Pre-trained Transformer-based Language Models, in EMNLP 2022. [\[pdf\]](https://arxiv.org/abs/2211.07349) - Do Prompts Solve NLP Tasks Using Natural Languages? in Arxiv 2022. [\[pdf\]](https://arxiv.org/abs/2203.00902) - Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? in EMNLP 2022. [\[pdf\]](https://arxiv.org/abs/2202.12837) - Do Prompt-Based Models Really Understand the Meaning of their Prompts? in NAACL 2022. [\[pdf\]](https://arxiv.org/abs/2109.01247) - When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories, in arxiv 2022. [\[pdf\]](https://akariasai.github.io/files/llm_memorization.pdf) - Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers，in arxiv 2022. [\[pdf\]](https://arxiv.org/pdf/2212.10559.pdf) - Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity, in ACL 2022. [\[pdf\]](https://aclanthology.org/2022.acl-long.556.pdf) ## Contact Information For help or issues using the tookits, please submit a GitHub issue.