# Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning arXiv: https://arxiv.org/abs/2205.02355 ยท HTML: https://mlsanity.com/paper/2205.02355/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 41 of its 53 references that are in the map. - First posted on arXiv 4 May 2022. ## Builds on (most cited first, 30 of 41) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017) - [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020) - [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019) - [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019) - [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021) - [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020) - [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019) - [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019) - [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021) - [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021) - [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020) - [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021) - [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021) - [Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://mlsanity.com/paper/2010.13641/) (2020) - [TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task](https://mlsanity.com/paper/2004.14855/) (2020) - [Efficient Nearest Neighbor Language Models](https://mlsanity.com/paper/2109.04212/) (2021) - [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021) - [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021) - [Re-TACRED: Addressing Shortcomings of the TACRED Dataset](https://mlsanity.com/paper/2104.08398/) (2021) - [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021) - [Dialogue-Based Relation Extraction](https://mlsanity.com/paper/2004.08056/) (2020) - [Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://mlsanity.com/paper/1903.01306/) (2019) - [Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval](https://mlsanity.com/paper/2201.12431/) (2022) - [Contrastive Triple Extraction with Generative Transformer](https://mlsanity.com/paper/2009.06207/) (2020) - [PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction](https://mlsanity.com/paper/2106.09895/) (2021) - [BERT-kNN: Adding a kNN Search Component to Pretrained Language Models for Better QA](https://mlsanity.com/paper/2005.00766/) (2020) ## Built on by (most cited first, 13 of 13) - [Generative Knowledge Graph Construction: A Review](https://mlsanity.com/paper/2210.12714/) (2022) - [Automated Construction of Theme-specific Knowledge Graphs](https://mlsanity.com/paper/2404.19146/) (2024) - [Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://mlsanity.com/paper/2210.10709/) (2022) - [Schema-adaptable Knowledge Graph Construction](https://mlsanity.com/paper/2305.08703/) (2023) - [Analogical Inference Enhanced Knowledge Graph Embedding](https://mlsanity.com/paper/2301.00982/) (2023) - [Enhancing Low-Resource Relation Representations through Multi-View Decoupling](https://mlsanity.com/paper/2312.17267/) (2023) - [FPT: Feature Prompt Tuning for Few-shot Readability Assessment](https://mlsanity.com/paper/2404.02772/) (2024) - [Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2407.02867/) (2024) - [Generating Diverse Training Samples for Relation Extraction with Large Language Models](https://mlsanity.com/paper/2505.23108/) (2025) - [Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification](https://mlsanity.com/paper/2312.04982/) (2023) - [BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction](https://mlsanity.com/paper/2401.14166/) (2024) - [A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction](https://mlsanity.com/paper/2408.02233/) (2024) - [M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models](https://mlsanity.com/paper/2509.07730/) (2025) ## Abstract (arXiv) Pre-trained language models have contributed significantly to relation extraction by demonstrating remarkable few-shot learning abilities. However, prompt tuning methods for relation extraction may still fail to generalize to those rare or hard patterns. Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test. Those long-tailed or hard patterns can hardly be memorized in parameters given few-shot instances. To this end, we regard RE as an open-book examination and propose a new semiparametric paradigm of retrieval-enhanced prompt tuning for relation extraction. We construct an open-book datastore for retrieval regarding prompt-based instance representations and corresponding relation labels as memorized key-value pairs. During inference, the model can infer relations by linearly interpolating the base output of PLM with the non-parametric nearest neighbor distribution over the datastore. In this way, our model not only infers relation through knowledge stored in the weights during training but also assists decision-making by unwinding and querying examples in the open-book datastore. Extensive experiments on benchmark datasets show that our method can achieve state-of-the-art in both standard supervised and few-shot settings. Code are available in https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE.