{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:19:41Z","timestamp":1743016781378,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031702389"},{"type":"electronic","value":"9783031702396"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70239-6_21","type":"book-chapter","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:02:09Z","timestamp":1726725729000},"page":"301-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["REA: Refine-Estimate-Answer Prompting for\u00a0Zero-Shot Relation Extraction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-974X","authenticated-orcid":false,"given":"Amirhossein","family":"Layegh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2748-8929","authenticated-orcid":false,"given":"Amir H.","family":"Payberah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4722-0823","authenticated-orcid":false,"given":"Mihhail","family":"Matskin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"21_CR1","unstructured":"Arora, S., Narayan, A., Chen, M.F., et\u00a0al.: Ask me anything: a simple strategy for prompting language models. In: ICLR (2023). https:\/\/openreview.net\/forum?id=bhUPJnS2g0X"},{"key":"21_CR2","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., et al.: Language models are few-shot learners. NeurIPS 33, 1877\u20131901 (2020)","journal-title":"NeurIPS"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Bunescu, R., Mooney, R.: A shortest path dependency kernel for relation extraction. In: EMNLP, pp. 724\u2013731 (2005)","DOI":"10.3115\/1220575.1220666"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Chen, C.Y., Li, C.T.: ZS-BERT: towards zero-shot relation extraction with attribute representation learning. In: NAACL, pp. 3470\u20133479 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.272"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Chia, Y.K., et\u00a0al.: RelationPrompt: leveraging prompts to generate synthetic data for zero-shot relation triplet extraction. In: Findings of the ACL (2022)","DOI":"10.18653\/v1\/2022.findings-acl.5"},{"key":"21_CR6","unstructured":"Deng, S., Ma, Y., Zhang, N., Cao, Y., Hooi, B.: Information extraction in low-resource scenarios: survey and perspective. arXiv preprint arXiv:2202.08063 (2022)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Dhuliawala, S., Komeili, M., et\u00a0al.: Chain-of-verification reduces hallucination in large language models. arXiv preprint arXiv:2309.11495 (2023)","DOI":"10.18653\/v1\/2024.findings-acl.212"},{"key":"21_CR8","unstructured":"Ding, N., Wang, X., Fu, Y., et\u00a0al.: Prototypical representation learning for relation extraction. In: ICLR (2021)"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Han, X., Zhu, H., et\u00a0al.: FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: EMNLP, pp. 4803\u20134809 (2018)","DOI":"10.18653\/v1\/D18-1514"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Huang, F., Kwak, H., An, J.: Chain of explanation: new prompting method to generate quality natural language explanation for implicit hate speech. In: The ACM Web Conference 2023","DOI":"10.1145\/3543873.3587320"},{"key":"21_CR11","unstructured":"Jiang, A.Q., Sablayrolles, A., et\u00a0al.: Mixtral of experts. arXiv preprint arXiv:2401.04088 (2024)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Khoo, C., Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction. In: The Semantics of Relationships: An Interdisciplinary Perspective, pp. 161\u2013180 (2002)","DOI":"10.1007\/978-94-017-0073-3_10"},{"key":"21_CR13","first-page":"22199","volume":"35","author":"T Kojima","year":"2022","unstructured":"Kojima, T., Gu, S.S., et al.: Large language models are zero-shot reasoners. NIPS 35, 22199\u201322213 (2022)","journal-title":"NIPS"},{"key":"21_CR14","unstructured":"Kuhn, L., Gal, Y., Farquhar, S.: Semantic uncertainty: linguistic invariances for uncertainty estimation in natural language generation. In: ICLR (2023)"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Layegh, A., Payberah, A.H., et\u00a0al.: Wiki-based prompts for enhancing relation extraction using language models. In: SAC 2024, Wiki-based Prompts for Enhancing Relation Extraction using Language Models (2024)","DOI":"10.1145\/3605098.3635949"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Levy, O., Seo, M., et\u00a0al.: Zero-shot relation extraction via reading comprehension. In: CoNLL, pp. 333\u2013342 (2017)","DOI":"10.18653\/v1\/K17-1034"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., et\u00a0al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Li, G., Wang, P., Ke, W.: Revisiting large language models as zero-shot relation extractors. In: Findings of EMNLP 2023, pp. 6877\u20136892 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.459"},{"key":"21_CR19","unstructured":"Li, W., Qian, T.: Generative meta-learning for zero-shot relation triplet extraction. arXiv preprint arXiv:2305.01920 (2023)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Liu, J., Shen, D., Others: What makes good in-context examples for GPT-3? In: DeeLIO 2022, pp. 100\u2013114 (2022)","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Liu, P., et\u00a0al.: Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. (2023)","DOI":"10.1145\/3560815"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Luo, D., Su, J., Yu, S.: A BERT-based approach with relation-aware attention for knowledge base question answering. In: 2020 IJCNN. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207186"},{"key":"21_CR23","unstructured":"Madaan, A., et\u00a0al.: Self-Refine: iterative refinement with self-feedback. In: NIPS (2024)"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Muhammad, I., Kearney, A., et\u00a0al.: Open information extraction for knowledge graph construction. In: DEXA, pp. 103\u2013113 (2020)","DOI":"10.1007\/978-3-030-59028-4_10"},{"key":"21_CR25","unstructured":"OpenAI: Introduce ChatGPT. OpenAI blog (2023). https:\/\/openai.com\/blog\/chatgpt"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Press, O., Zhang, M., et\u00a0al.: Measuring and narrowing the compositionality gap in language models. In: Findings of EMNLP, pp. 5687\u20135711 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.378"},{"issue":"8","key":"21_CR27","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1(8), 9 (2019)","journal-title":"OpenAI blog"},{"key":"21_CR28","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: EMNLP (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"21_CR30","unstructured":"Shi, F., Suzgun, M., et\u00a0al.: Language models are multilingual chain-of-thought reasoners. arXiv preprint arXiv:2210.03057 (2022)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Tian, K., et\u00a0al.: Just ask for calibration: strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. In: EMNLP (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.330"},{"key":"21_CR32","unstructured":"Vaswani, A., Shazeer, N., et\u00a0al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Wang, W., Zheng, V.W., et\u00a0al.: A survey of zero-shot learning: settings, methods, and applications. In: ACM TIST, pp. 1\u201337 (2019)","DOI":"10.1145\/3293318"},{"key":"21_CR34","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., et al.: Chain-of-thought prompting elicits reasoning in large language models. NIPS 35, 24824\u201324837 (2022)","journal-title":"NIPS"},{"key":"21_CR35","unstructured":"Wei, X., Cui, X., et\u00a0al.: Zero-shot information extraction via chatting with ChatGPT. arXiv preprint arXiv:2302.10205 (2023)"},{"key":"21_CR36","unstructured":"Xu, D., Chen, W.: Large language models for generative information extraction: a survey. arXiv preprint arXiv:2312.17617 (2023)"},{"key":"21_CR37","unstructured":"Yasunaga, M., Chen, X., Li, Y., Pasupat, P., Leskovec, J., et\u00a0al.: Large language models as analogical reasoners. arXiv preprint arXiv:2310.01714 (2023)"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhang, H., et\u00a0al.: Chain-of-Note: enhancing robustness in retrieval-augmented language models. arXiv preprint arXiv:2311.09210 (2023)","DOI":"10.18653\/v1\/2024.emnlp-main.813"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, K., Jimenez\u00a0Gutierrez, B.: Aligning instruction tasks unlocks large language models as zero-shot relation extractors. In: Findings of ACL (2023)","DOI":"10.18653\/v1\/2023.findings-acl.50"},{"key":"21_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhong, V., et\u00a0al.: Position-aware attention and supervised data improve slot filling. In: Proceedings of EMNLP Conference (2017)","DOI":"10.18653\/v1\/D17-1004"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhan, W., Zhao, W.X., et\u00a0al.: Re-matching: a fine-grained semantic matching method for zero-shot relation extraction. In: ACL, pp. 6680\u20136691 (2023)","DOI":"10.18653\/v1\/2023.acl-long.369"},{"key":"21_CR42","unstructured":"Zhou, D., Sch\u00e4rli, N., et\u00a0al.: Least-to-most prompting enables complex reasoning in large language models. In: ICLR (2022)"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Zhou, W., Chen, M.: An improved baseline for sentence-level relation extraction. In: ACL (Short Papers), pp. 161\u2013168 (2022)","DOI":"10.18653\/v1\/2022.aacl-short.21"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70239-6_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T10:24:24Z","timestamp":1732789464000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70239-6_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031702389","9783031702396"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70239-6_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLDB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applications of Natural Language to Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nldb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/nldb2024.di.unito.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}