{"type":"ScholarlyArticle","title":"Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors","authors":[{"name":"Guozheng Li","slug":"guozheng-li","given":"Guozheng","family":"Li","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Peng Wang","slug":"peng-wang","given":"Peng","family":"Wang","affiliation":"School of Computer Science and Engineering , Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University) , Ministry of Education"},{"name":"Jiajun Liu","slug":"jiajun-liu","given":"Jiajun","family":"Liu","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Yikai Guo","slug":"yikai-guo","given":"Yikai","family":"Guo","affiliation":"Beijing Institute of Computer Technology and Application"},{"name":"Ke Ji","slug":"ke-ji","given":"Ke","family":"Ji","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Ziyu Shang","slug":"ziyu-shang","given":"Ziyu","family":"Shang","affiliation":"School of Computer Science and Engineering , Southeast University"},{"name":"Zijie Xu","slug":"zijie-xu","given":"Zijie","family":"Xu","affiliation":"School of Computer Science and Engineering , Southeast University"}],"abstract":"Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-context learning (ICL) capability for general zero and few-shot learning, recent studies indicate that current LLMs still struggle with zero and few-shot RE. Previous studies are mainly dedicated to design prompt formats and select good examples for improving ICL-based RE. Although both factors are vital for ICL, if one can fundamentally boost the ICL capability of LLMs in RE, the zero and few-shot RE performance via ICL would be significantly improved. To this end, we introduce Micre (Meta In-Context learning of LLMs for Relation Extraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e., learning to learn in context for RE). Through meta-training, the model becomes more effectively to learn a new RE task in context by conditioning on a few training examples with no parameter updates or task-specific templates at inference time, enabling better zero and few-shot task generalization. We experiment Micre on various LLMs with different model scales and 12 public RE datasets, and then evaluate it on unseen RE benchmarks under zero and few-shot settings. Micre delivers comparable or superior performance compared to a range of baselines including supervised fine-tuning and typical in-context learning methods. We find that the gains are particular significant for larger model scales, and using a diverse set of the meta-training RE datasets is key to improvements. Empirically, we show that Micre can transfer the relation semantic knowledge via relation label name during inference on target RE datasets.","doi":"10.24963/ijcai.2024/702","url":"https://ijcai.org/proceedings/2024/702","pdf":"https://ijcai.org/proceedings/2024/0702.pdf","markdown":"https://ijcai.org/proceedings/2024/702.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/702","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":702,"pages":{"first":6350,"last":6358},"keywords":["Natural Language Processing: NLP: Information extraction"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 2022] Monica Agrawal, Stefan Hegsel- mann, et al. Large language models are few-shot clinical information extractors. In EMNLP, 2022.","title":"Large language models are few-shot clinical information extractors","authors":"Monica Agrawal, Stefan Hegselmann","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, et al. Language models are few-shot learners. In NeurIPS, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen and Li, 2021] Chih-Yao Chen and Cheng-Te Li. ZS- BERT: Towards zero-shot relation extraction with attribute representation learning. In NAACL-HLT, 2021.","title":"ZS-BERT: Towards zero-shot relation extraction with attribute representation learning","authors":"Chen, Li ; ] Chih-Yao Chen, Cheng-Te Li","year":"2021","venue":"NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2022] Yanda Chen, Ruiqi Zhong, et al. Meta- learning via language model in-context tuning. In ACL, 2022.","title":"Metalearning via language model in-context tuning","authors":"Yanda Chen, Ruiqi Zhong","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chia et al., 2022] Yew Ken Chia, Lidong Bing, et al. Re- lationprompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction. In Findings of ACL, 2022.","title":"Relationprompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction","authors":"Ken Yew, Lidong Chia, Bing","year":"2022","venue":"Findings of ACL","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cong et al., 2021] Xin Cong, Shiyao Cui, et al. Few-shot event detection with prototypical amortized conditional random field. In Findings of ACL, 2021.","title":"Few-shot event detection with prototypical amortized conditional random field","authors":"Xin Cong, Shiyao Cui","year":"2021","venue":"Findings of ACL","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cong et al., 2022] Xin Cong, Jiawei Sheng, et al. Relation- guided few-shot relational triple extraction. In SIGIR, 2022.","title":"Relationguided few-shot relational triple extraction","authors":"Xin Cong, Jiawei Sheng","year":"2022","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT, 2019.","title":"Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang","year":"2019","venue":"NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Doddington et al., 2004] George R Doddington, Alexis Mitchell, et al. The automatic content extraction (ace) program-tasks, data, and evaluation. In LREC, 2004.","title":"The automatic content extraction (ace) program-tasks, data, and evaluation","authors":"Alexis George R Doddington, Mitchell","year":"2004","venue":"LREC","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Du et al., 2022] Zhengxiao Du, Yujie Qian, et al. GLM: General language model pretraining with autoregressive blank infilling. In ACL, 2022.","title":"GLM: General language model pretraining with autoregressive blank infilling","authors":"Du","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gardent et al., 2017] Claire Gardent, Anastasia Shimorina, et al. The webnlg challenge: Generating text from rdf data. In INLG, 2017.","title":"The webnlg challenge: Generating text from rdf data","authors":"Claire Gardent, Anastasia Shimorina","year":"2017","venue":"INLG","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gurulingappa et al., 2012] Harsha Gurulingappa, Ab- dul Mateen Rajput, et al. Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of biomedical informatics, 45(5):885-892, 2012.","title":"Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports","authors":"Harsha Gurulingappa, Abdul Mateen Rajput","year":"2012","venue":"Journal of biomedical informatics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Han et al., 2018] Xu Han, Hao Zhu, et al. FewRel: A large- scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In EMNLP, 2018.","title":"FewRel: A largescale supervised few-shot relation classification dataset with state-of-the-art evaluation","authors":"Xu Han, Hao Zhu","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Han et al., 2021] Jiale Han, Bo Cheng, and Wei Lu. Ex- ploring task difficulty for few-shot relation extraction. In EMNLP, 2021.","title":"Exploring task difficulty for few-shot relation extraction","authors":"Han","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hendrickx et al., 2019] Iris Hendrickx, Su Nam Kim, et al. Semeval-2010 task 8: Multi-way classification of seman- tic relations between pairs of nominals. arXiv preprint arXiv:1911.10422, 2019.","title":"Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals","authors":"Hendrickx","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hu et al., 2022] Edward J Hu, Yelong Shen, et al. Lora: Low-rank adaptation of large language models. In ICLR, 2022.","title":"Lora: Low-rank adaptation of large language models","authors":"J Edward, Yelong Hu, Shen","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jat et al., 2018] Sharmistha Jat, Siddhesh Khandelwal, and Partha Talukdar. Improving distantly supervised relation extraction using word and entity based attention. arXiv preprint arXiv:1804.06987, 2018.","title":"Improving distantly supervised relation extraction using word and entity based attention","authors":"Sharmistha Jat, Siddhesh Khandelwal, Partha Talukdar","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ji et al., 2023] Ke Ji, Yixin Lian, et al. Hierarchical verbal- izer for few-shot hierarchical text classification. In ACL, 2023.","title":"Hierarchical verbalizer for few-shot hierarchical text classification","authors":"Ke Ji, Yixin Lian","year":"2023","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kim et al., 2023] Bosung Kim, Hayate Iso, et al. Zero-shot triplet extraction by template infilling. In AACL, 2023.","title":"Zero-shot triplet extraction by template infilling","authors":"Kim","year":"2023","venue":"AACL","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kojima et al., 2022] Takeshi Kojima, Shixiang Shane Gu, et al. Large language models are zero-shot reasoners. In NeurIPS, 2022.","title":"Large language models are zero-shot reasoners","authors":"Takeshi Kojima, Shixiang Shane Gu","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Levy et al., 2017] Omer Levy, Minjoon Seo, et al. Zero- shot relation extraction via reading comprehension. In CoNLL, 2017.","title":"Zeroshot relation extraction via reading comprehension","authors":"Omer Levy, Minjoon Seo","year":"2017","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Li et al., 2022] Guozheng Li, Xu Chen, et al. Fastre: To- wards fast relation extraction with convolutional encoder and improved cascade binary tagging framework. In IJ- CAI, 2022.","title":"Fastre: Towards fast relation extraction with convolutional encoder and improved cascade binary tagging framework","authors":"Li","year":"2022","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2022/583"},{"position":24,"raw":"[Li et al., 2023] Guozheng Li, Peng Wang, and Wenjun Ke. Revisiting large language models as zero-shot relation ex- tractors. In Findings of EMNLP, 2023.","title":"Revisiting large language models as zero-shot relation extractors","authors":"Guozheng Li, Peng Wang, Wenjun Ke","year":"2023","venue":"Findings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2024] Guozheng Li, Wenjun Ke, et al. Unlock- ing instructive in-context learning with tabular prompt- ing for relational triple extraction. arXiv preprint arXiv:2402.13741, 2024.","title":"Unlocking instructive in-context learning with tabular prompting for relational triple extraction","authors":"Li","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2019] Yinhan Liu, Myle Ott, et al. Roberta: A ro- bustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692, 2019.","title":"Roberta: A robustly optimized bert pretraining approach","authors":"Liu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2024] Jiajun Liu, Wenjun Ke, et al. Towards con- tinual knowledge graph embedding via incremental distil- lation. In AAAI, 2024.","title":"Towards continual knowledge graph embedding via incremental distillation","authors":"Jiajun Liu, Wenjun Ke","year":"2024","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Luan et al., 2018] Yi Luan, Luheng He, et al. Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. arXiv preprint arXiv:1808.09602, 2018.","title":"Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction","authors":"Yi Luan, Luheng He","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Ma et al., 2023a] Xilai Ma, Jing Li, and Min Zhang. Chain of thought with explicit evidence reasoning for few-shot relation extraction. In Findings of EMNLP, 2023.","title":"Chain of thought with explicit evidence reasoning for few-shot relation extraction","authors":"Ma","year":"2023","venue":"Findings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ma et al., 2023b] Yubo Ma, Yixin Cao, et al. Large lan- guage model is not a good few-shot information extrac- tor, but a good reranker for hard samples! In Findings of EMNLP, 2023.","title":"Large language model is not a good few-shot information extractor, but a good reranker for hard samples! In Findings of EMNLP","authors":"Ma","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Min et al., 2022] Sewon Min, Mike Lewis, et al. Metaicl: Learning to learn in context. In NAACL-HLT, 2022.","title":"Metaicl: Learning to learn in context","authors":"Sewon Min, Mike Lewis","year":"2022","venue":"NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mishra et al., 2022] Swaroop Mishra, Daniel Khashabi, et al. Cross-task generalization via natural language crowdsourcing instructions. In ACL, 2022.","title":"Cross-task generalization via natural language crowdsourcing instructions","authors":"Swaroop Mishra, Daniel Khashabi","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[OpenAI, 2022] OpenAI. Introducing chatgpt, 2022.","title":"OpenAI. Introducing chatgpt","authors":"Openai","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Peng et al., 2020] Hao Peng, Tianyu Gao, et al. Learning from context or names? an empirical study on neural rela- tion extraction. In ACL, 2020.","title":"Learning from context or names? an empirical study on neural relation extraction","authors":"Hao Peng, Tianyu Gao","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Radford et al., 2019] Alec Radford, Jeffrey Wu, et al. Lan- guage models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.","title":"Language models are unsupervised multitask learners","authors":"Alec Radford, Jeffrey Wu","year":"2019","venue":"OpenAI blog","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Raffel et al., 2020] Colin Raffel, Noam Shazeer, et al. Ex- ploring the limits of transfer learning with a unified text- to-text transformer. JMLR, 21(1):5485-5551, 2020.","title":"Exploring the limits of transfer learning with a unified textto-text transformer","authors":"Colin Raffel, Noam Shazeer","year":"2020","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Rasley et al., 2020] Jeff Rasley, Samyam Rajbhandari, et al. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In SIGKDD, 2020.","title":"Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters","authors":"Jeff Rasley, Samyam Rajbhandari","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Riedel et al., 2010] Sebastian Riedel, Limin Yao, and An- drew McCallum. Modeling relations and their mentions without labeled text. In ECML-PKDD, 2010.","title":"Modeling relations and their mentions without labeled text","authors":"Sebastian Riedel, Limin Yao, Andrew Mccallum","year":"2010","venue":"ECML-PKDD","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Roth and Yih, 2004] Dan Roth and Wen-tau Yih. A linear programming formulation for global inference in natural language tasks. In CoNLL, 2004.","title":"A linear programming formulation for global inference in natural language tasks","authors":"Dan Roth, Wen-Tau Yih","year":"2004","venue":"CoNLL","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sainz et al., 2021] Oscar Sainz, Oier Lopez de Lacalle, et al. Label verbalization and entailment for effective zero- and few-shot relation extraction. In EMNLP, 2021.","title":"Label verbalization and entailment for effective zeroand few-shot relation extraction","authors":"Sainz","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Sanh et al., 2022] Victor Sanh, Albert Webson, et al. Mul- titask prompted training enables zero-shot task generaliza- tion. In ICLR, 2022.","title":"Multitask prompted training enables zero-shot task generalization","authors":"Victor Sanh, Albert Webson","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Shang et al., 2024] Ziyu Shang, Wenjun Ke, et al. Ontofact: Unveiling fantastic fact-skeleton of llms via ontology- driven reinforcement learning. In AAAI, 2024.","title":"Ontofact: Unveiling fantastic fact-skeleton of llms via ontologydriven reinforcement learning","authors":"Ziyu Shang, Wenjun Ke","year":"2024","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Snell et al., 2017] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NIPS, 2017.","title":"Prototypical networks for few-shot learning","authors":"Jake Snell, Kevin Swersky, Richard Zemel","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Takanobu et al., 2019] Ryuichi Takanobu, Tianyang Zhang, et al. A hierarchical framework for relation extraction with reinforcement learning. In AAAI, 2019.","title":"A hierarchical framework for relation extraction with reinforcement learning","authors":"Ryuichi Takanobu, Tianyang Zhang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Touvron et al., 2023] Hugo Touvron, Thibaut Lavril, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.","title":"Hugo Touvron, Thibaut Lavril, et al. Llama: Open and efficient foundation language models","authors":"Touvron","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, et al. Attention is all you need. In NIPS, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Walker et al., 2005] Christopher Walker, Stephanie Strassel, et al. Ace 2005 multilingual training corpus- linguistic data consortium. URL: https://catalog. ldc. upenn. edu/LDC2006T06, 2005.","title":"Ace 2005 multilingual training corpuslinguistic data consortium","authors":"Christopher Walker, Stephanie Strassel","year":"2005","venue":null,"doi":null,"url":"https://catalog.ldc.upenn.edu/LDC2006T06","internal_url":null},{"position":48,"raw":"[Wan et al., 2023] Zhen Wan, Fei Cheng, et al. GPT-RE: In-context learning for relation extraction using large lan- guage models. In EMNLP, 2023.","title":"GPT-RE: In-context learning for relation extraction using large language models","authors":"Zhen Wan, Fei Cheng","year":"2023","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wang and Lu, 2020] Jue Wang and Wei Lu. Two are better than one: Joint entity and relation extraction with table- sequence encoders. In EMNLP, 2020.","title":"Two are better than one: Joint entity and relation extraction with tablesequence encoders","authors":"Jue Wang, Wei Lu","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wang et al., 2022] Chenguang Wang, Xiao Liu, et al. Deep- struct: Pretraining of language models for structure predic- tion. In Findings of ACL, 2022.","title":"Deepstruct: Pretraining of language models for structure prediction","authors":"Chenguang Wang, Xiao Liu","year":"2022","venue":"Findings of ACL","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Wang et al., 2023a] Peng Wang, Tong Shao, et al. fmlre: a low-resource relation extraction model based on feature mapping similarity calculation. In AAAI, 2023.","title":"fmlre: a low-resource relation extraction model based on feature mapping similarity calculation","authors":"Wang","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wang et al., 2023b] Peng Wang, Jiafeng Xie, et al. Pascore: a chinese overlapping relation extraction model based on global pointer annotation strategy. In IJCAI, 2023.","title":"Pascore: a chinese overlapping relation extraction model based on global pointer annotation strategy","authors":"Wang","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/579"},{"position":53,"raw":"[Wang et al., 2023c] Xiao Wang, Weikang Zhou, et al. In- structuie: Multi-task instruction tuning for unified in- formation extraction. arXiv preprint arXiv:2304.08085, 2023.","title":"Instructuie: Multi-task instruction tuning for unified information extraction","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Wei et al., 2020] Zhepei Wei, Jianlin Su, et al. A novel cas- cade binary tagging framework for relational triple extrac- tion. In ACL, 2020.","title":"A novel cascade binary tagging framework for relational triple extraction","authors":"Wei","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Wei et al., 2022a] Jason Wei, Maarten Bosma, et al. Fine- tuned language models are zero-shot learners. In ICLR, 2022.","title":"Finetuned language models are zero-shot learners","authors":"Wei","year":"2022","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Wei et al., 2022b] Jason Wei, Xuezhi Wang, et al. Chain of thought prompting elicits reasoning in large language models. In NeurIPS, 2022.","title":"Chain of thought prompting elicits reasoning in large language models","authors":"Wei","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Yang and Katiyar, 2020] Yi Yang and Arzoo Katiyar. Sim- ple and effective few-shot named entity recognition with structured nearest neighbor learning. In EMNLP, 2020.","title":"Simple and effective few-shot named entity recognition with structured nearest neighbor learning","authors":"Yang, Katiyar ; ] Yi, Yang, Arzoo Katiyar","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Yang et al., 2023] Chengmei Yang, Shuai Jiang, et al. Mu- tually guided few-shot learning for relational triple extrac- tion. In ICASSP, 2023.","title":"Mutually guided few-shot learning for relational triple extraction","authors":"Yang","year":"2023","venue":"ICASSP","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Yu et al., 2020] Haiyang Yu, Ningyu Zhang, et al. Bridging text and knowledge with multi-prototype embedding for few-shot relational triple extraction. In COLING, 2020.","title":"Bridging text and knowledge with multi-prototype embedding for few-shot relational triple extraction","authors":"Haiyang Yu, Ningyu Zhang","year":"2020","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zhang and Lu, 2022] Peiyuan Zhang and Wei Lu. Better few-shot relation extraction with label prompt dropout. In EMNLP, 2022.","title":"Better few-shot relation extraction with label prompt dropout","authors":"Lu ; ] Peiyuan Zhang, Wei Zhang, Lu","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Zhang and Wang, 2015] Dongxu Zhang and Dong Wang. Relation classification via recurrent neural network. arXiv preprint arXiv:1508.01006, 2015.","title":"Relation classification via recurrent neural network","authors":"Wang ; Zhang, Dongxu Zhang, Dong Wang","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Zhang et al., 2017] Yuhao Zhang, Victor Zhong, et al. Position-aware attention and supervised data improve slot filling. In EMNLP, 2017.","title":"Position-aware attention and supervised data improve slot filling","authors":"Yuhao Zhang, Victor Zhong","year":"2017","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Zhang et al., 2023] Liang Zhang, Chulun Zhou, et al. HyperNetwork-based decoupling to improve model gen- eralization for few-shot relation extraction. In EMNLP, 2023.","title":"HyperNetwork-based decoupling to improve model generalization for few-shot relation extraction","authors":"Liang Zhang, Chulun Zhou","year":"2023","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Zhao et al., 2021] Zihao Zhao, Eric Wallace, et al. Calibrate before use: Improving few-shot performance of language models. In ICML, 2021.","title":"Calibrate before use: Improving few-shot performance of language models","authors":"Zhao","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Zhao et al., 2023] Jun Zhao, WenYu Zhan, et al. RE- matching: A fine-grained semantic matching method for zero-shot relation extraction. In ACL, 2023.","title":"REmatching: A fine-grained semantic matching method for zero-shot relation extraction","authors":"Zhao","year":"2023","venue":"ACL","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}