{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T11:31:58Z","timestamp":1763724718225,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031723438"},{"type":"electronic","value":"9783031723445"}],"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\/978-3-031-72344-5_22","type":"book-chapter","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T14:03:01Z","timestamp":1726495381000},"page":"324-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generative Chain-of-Thought for\u00a0Zero-Shot Cognitive Reasoning"],"prefix":"10.1007","author":[{"given":"Liang","family":"Liu","sequence":"first","affiliation":[]},{"given":"Dong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Suyang","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Shoushan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"22_CR1","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., et\u00a0al.: Language models are few-shot learners. In: Proceedings of NeurIPS 2020, pp. 1877\u20131901 (2020)"},{"key":"22_CR2","unstructured":"Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., et\u00a0al.: PaLM: scaling language modeling with pathways. JMLR 24, 240:1\u2013240:113 (2023)"},{"key":"22_CR3","unstructured":"Chung, H.W., Hou, L., Longpre, S., Zoph, B., Tay, Y., et\u00a0al.: Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022)"},{"key":"22_CR4","unstructured":"Deng, P., Yuan, J., Zhao, Y., Qin, B.: Zero-shot aspect-level sentiment classification via explicit utilization of aspect-to-document sentiment composition. arXiv preprint arXiv:2209.02276 (2022)"},{"issue":"10","key":"22_CR5","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.tics.2003.08.012","volume":"7","author":"JSB Evans","year":"2003","unstructured":"Evans, J.S.B.: In two minds: dual-process accounts of reasoning. Trends Cogn. Sci. 7(10), 454\u2013459 (2003)","journal-title":"Trends Cogn. Sci."},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Fei, H., Li, B., Liu, Q., Bing, L., Li, F., et\u00a0al.: Reasoning implicit sentiment with chain-of-thought prompting. In: Proceedings of ACL 2023, pp. 1171\u20131182 (2023)","DOI":"10.18653\/v1\/2023.acl-short.101"},{"key":"22_CR7","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1162\/tacl_a_00370","volume":"9","author":"M Geva","year":"2021","unstructured":"Geva, M., Khashabi, D., Segal, E., Khot, T., Roth, D., et al.: Did aristotle use a laptop? A question answering benchmark with implicit reasoning strategies. TACL 9, 346\u2013361 (2021)","journal-title":"TACL"},{"key":"22_CR8","unstructured":"Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., et\u00a0al.: Training compute-optimal large language models. arXiv preprint arXiv:2203.15556 (2022)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Hosseini, M.J., Hajishirzi, H., Etzioni, O., Kushman, N.: Learning to solve arithmetic word problems with verb categorization. In: Proceedings of EMNLP 2014, pp. 523\u2013533 (2014)","DOI":"10.3115\/v1\/D14-1058"},{"key":"22_CR10","unstructured":"Jiang, A.Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D.S., et\u00a0al.: Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)"},{"key":"22_CR11","unstructured":"Khot, T., Trivedi, H., Finlayson, M., Fu, Y., Richardson, K., et\u00a0al.: Decomposed prompting: a modular approach for solving complex tasks. In: Proceedings of ICLR 2023 (2023)"},{"key":"22_CR12","unstructured":"Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Proceedings of NeurIPS 2022, pp. 22199\u201322213 (2022)"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Ling, W., Yogatama, D., Dyer, C., Blunsom, P.: Program induction by rationale generation: learning to solve and explain algebraic word problems. In: Proceedings of ACL 2017, pp. 158\u2013167 (2017)","DOI":"10.18653\/v1\/P17-1015"},{"key":"22_CR14","unstructured":"Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., et\u00a0al.: Training language models to follow instructions with human feedback. In: Proceedings of NeurIPS 2022, pp. 27730\u201327744 (2022)"},{"key":"22_CR15","unstructured":"Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., et\u00a0al.: The refinedweb dataset for falcon LLM: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116 (2023)"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., et\u00a0al.: SemEval-2014 Task 4: aspect based sentiment analysis. In: Proceedings of COLING 2014, pp. 27\u201335 (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"22_CR17","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., et\u00a0al.: Language models are unsupervised multitask learners. OpenAI blog (2019)"},{"key":"22_CR18","unstructured":"Rae, J.W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., et\u00a0al.: Scaling language models: methods, analysis & insights from training gopher. arXiv preprint arXiv:2112.11446 (2021)"},{"key":"22_CR19","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., et\u00a0al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21, 140:1\u2013140:67 (2020)"},{"key":"22_CR20","unstructured":"Scao, T.L., Fan, A., Akiki, C., Pavlick, E., Ilic, S., et\u00a0al.: BLOOM: a 176B-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100 (2022)"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Seoh, R., Birle, I., Tak, M., Chang, H., Pinette, B., et\u00a0al.: Open aspect target sentiment classification with natural language prompts. In: Proceedings of EMNLP 2021, pp. 6311\u20136322 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.509"},{"key":"22_CR22","unstructured":"Shi, F., Suzgun, M., Freitag, M., Wang, X., Srivats, S., et\u00a0al.: Language models are multilingual chain-of-thought reasoners. In: Proceedings of ICLR 2023 (2023)"},{"key":"22_CR23","unstructured":"Shu, L., Xu, H., Liu, B., Chen, J.: Zero-shot aspect-based sentiment analysis. arXiv preprint arXiv:2202.01924 (2022)"},{"key":"22_CR24","unstructured":"Talmor, A., Herzig, J., Lourie, N., Berant, J.: CommonsenseQA: a question answering challenge targeting commonsense knowledge. In: Proceedings of NAACL-HLT 2019, pp. 4149\u20134158 (2019)"},{"key":"22_CR25","unstructured":"Tay, Y., Dehghani, M., Tran, V.Q., Garcia, X., Wei, J., et\u00a0al.: UL2: unifying language learning paradigms. In: Proceedings of ICLR 2023 (2023)"},{"key":"22_CR26","unstructured":"Thoppilan, R., Freitas, D.D., Hall, J., Shazeer, N., Kulshreshtha, A., et\u00a0al.: LaMDA: language models for dialog applications. arXiv preprint arXiv:2201.08239 (2022)"},{"key":"22_CR27","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M., et\u00a0al.: LLaMA: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"22_CR28","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., et\u00a0al.: LLaMA 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"key":"22_CR29","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., et\u00a0al.: Attention is all you need. In: Proceedings of NeurIPS 2017, pp. 5998\u20136008 (2017)"},{"key":"22_CR30","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511529863","volume-title":"Similarity and Analogical Reasoning","author":"S Vosniadou","year":"1989","unstructured":"Vosniadou, S., Ortony, A.: Similarity and Analogical Reasoning. Cambridge University Press, Cambridge (1989)"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Wang, L., Xu, W., Lan, Y., Hu, Z., Lan, Y., et\u00a0al.: Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models. In: Proceedings of ACL 2023, pp. 2609\u20132634 (2023)","DOI":"10.18653\/v1\/2023.acl-long.147"},{"key":"22_CR32","unstructured":"Wang, X., Wei, J., Schuurmans, D., Le, Q.V., Chi, E.H., et\u00a0al.: Self-consistency improves chain of thought reasoning in language models. In: Proceedings of ICLR 2023 (2023)"},{"key":"22_CR33","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., et\u00a0al.: Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of NeurIPS 2022, pp. 24824\u201324837 (2022)"},{"key":"22_CR34","unstructured":"Zhang, Z., Zhang, A., Li, M., Smola, A.: Automatic chain of thought prompting in large language models. In: Proceedings of ICLR 2023 (2023)"},{"key":"22_CR35","unstructured":"Zheng, L., Chiang, W., Sheng, Y., Zhuang, S., Wu, Z., et\u00a0al.: Judging LLM-as-a-judge with MT-bench and chatbot arena. arXiv preprint arXiv:2306.05685 (2023)"},{"key":"22_CR36","unstructured":"Zhou, D., Sch\u00e4rli, N., Hou, L., Wei, J., Scales, N., et\u00a0al.: Least-to-most prompting enables complex reasoning in large language models. In: Proceedings of ICLR 2023 (2023)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72344-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T14:07:44Z","timestamp":1726495664000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72344-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723438","9783031723445"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72344-5_22","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":"17 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lugano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Switzerland","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":"17 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}