{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:04:32Z","timestamp":1750219472140,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819681853","type":"print"},{"value":"9789819681860","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8186-0_21","type":"book-chapter","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:17:04Z","timestamp":1750155424000},"page":"260-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Analogy-Augmented Generation with\u00a0Procedural Memory for\u00a0Procedure Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1104-4052","authenticated-orcid":false,"given":"K.","family":"Roth","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1402-0426","authenticated-orcid":false,"given":"Rushil","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5300-7340","authenticated-orcid":false,"given":"Simon","family":"Halle","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9483-8984","authenticated-orcid":false,"given":"Bang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"21_CR1","unstructured":"Asai, A., Wu, Z., Wang, Y., Sil, A., Hajishirzi, H.: Self-rag: learning to retrieve, generate, and critique through self-reflection (2023)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Bhavya, B., Xiong, J., Zhai, C.: Analogy generation by prompting large language models: a case study of instructGPT. arXiv preprint arXiv:2210.04186 (2022)","DOI":"10.18653\/v1\/2022.inlg-main.25"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Bhavya, B., Xiong, J., Zhai, C.: CAM: a large language model-based creative analogy mining framework. In: Proceedings of the ACM Web Conference 2023, pp. 3903\u20133914 (2023)","DOI":"10.1145\/3543507.3587431"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Bie\u0144, M., Gilski, M., Maciejewska, M., Taisner, W., Wisniewski, D., Lawrynowicz, A.: RecipeNLG: a cooking recipes dataset for semi-structured text generation. In: Proceedings of the 13th International Conference on Natural Language Generation. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.inlg-1.4"},{"key":"21_CR5","unstructured":"Brahman, F., et al.: PlaSma: making small language models better procedural knowledge models for (counterfactual) planning (2023)"},{"key":"21_CR6","unstructured":"Chase, H., contributors: Langchain (October 2022). https:\/\/github.com\/langchain-ai\/langchain, version 0.0.249"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Gao, L., Ma, X., Lin, J., Callan, J.: Precise zero-shot dense retrieval without relevance labels (2022)","DOI":"10.18653\/v1\/2023.acl-long.99"},{"key":"21_CR8","unstructured":"Gao, Y., et al.: Retrieval-augmented generation for large language models: a survey (2024)"},{"key":"21_CR9","doi-asserted-by":"publisher","unstructured":"Gentner, D., Smith, L.: Analogical reasoning. In: Ramachandran, V. (ed.) Encyclopedia of Human Behavior (Second Edition), pp. 130\u2013136. Academic Press, San Diego, second edition edn. (2012). https:\/\/doi.org\/10.1016\/B978-0-12-375000-6.00022-7, https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780123750006000227","DOI":"10.1016\/B978-0-12-375000-6.00022-7"},{"key":"21_CR10","doi-asserted-by":"publisher","unstructured":"Gentner, D.: Structure-mapping: a theoretical framework for analogy. Cogn. Sci. 7(2), 155\u2013170 (1983). https:\/\/doi.org\/10.1207\/s15516709cog0702_3, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1207\/s15516709cog0702_3","DOI":"10.1207\/s15516709cog0702_3"},{"issue":"10","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1109\/PROC.1986.13639","volume":"74","author":"M Georgeff","year":"1986","unstructured":"Georgeff, M., Lansky, A.: Procedural knowledge. Proc. IEEE 74(10), 1383\u20131398 (1986). https:\/\/doi.org\/10.1109\/PROC.1986.13639","journal-title":"Proc. IEEE"},{"key":"21_CR12","unstructured":"Huang, W., Abbeel, P., Pathak, D., Mordatch, I.: Language models as zero-shot planners: extracting actionable knowledge for embodied agents. In: Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. (eds.) Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0162, pp. 9118\u20139147. PMLR (17\u201323 Jul 2022). https:\/\/proceedings.mlr.press\/v162\/huang22a.html"},{"key":"21_CR13","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks (2021)"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Ma, X., Gong, Y., He, P., Zhao, H., Duan, N.: Query rewriting for retrieval-augmented large language models (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"key":"21_CR15","unstructured":"Madaan, A., et al.: Self-refine: iterative refinement with self-feedback. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a036, pp. 46534\u201346594. Curran Associates, Inc. (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/91edff07232fb1b55a505a9e9f6c0ff3-Paper-Conference.pdf"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Mao, Y., Kim, Y., Zhou, Y.: Champ: a competition-level dataset for fine-grained analyses of LLMs\u2019 mathematical reasoning capabilities (2024)","DOI":"10.18653\/v1\/2024.findings-acl.785"},{"key":"21_CR17","unstructured":"OpenAI: GPT-3.5 turbo (2024). https:\/\/platform.openai.com\/docs\/models\/gpt-3-5-turbo. Accessed 15 Oct 2024"},{"issue":"3","key":"21_CR18","doi-asserted-by":"publisher","DOI":"10.1111\/cogs.13116","volume":"46","author":"J Parsons","year":"2022","unstructured":"Parsons, J., Davies, J.: The neural correlates of analogy component processes. Cogn. Sci. 46(3), e13116 (2022). https:\/\/doi.org\/10.1111\/cogs.13116","journal-title":"Cogn. Sci."},{"key":"21_CR19","unstructured":"Pavese, C.: Knowledge How. In: Zalta, E.N., Nodelman, U. (eds.) The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Fall 2022 edn. (2022)"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019). https:\/\/arxiv.org\/abs\/1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"key":"21_CR21","unstructured":"Saunders, W., et al.: Self-critiquing models for assisting human evaluators (2022)"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., Chen, W.: Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.620"},{"key":"21_CR23","unstructured":"Tan, W., et al.: Towards general computer control: a multimodal agent for red dead redemption ii as a case study. arXiv preprint arXiv: 2403.03186 (2024)"},{"key":"21_CR24","doi-asserted-by":"publisher","unstructured":"Wang, B., Yue, X., Sun, H.: Can ChatGPT defend its belief in truth? evaluating LLM reasoning via debate. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 11865\u201311881. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.795, https:\/\/aclanthology.org\/2023.findings-emnlp.795","DOI":"10.18653\/v1\/2023.findings-emnlp.795"},{"key":"21_CR25","doi-asserted-by":"publisher","unstructured":"Wang, L., Ma, C., Feng, X., et\u00a0al.: A survey on large language model based autonomous agents. Front. Comput. Sci. 18, 186345 (2024). https:\/\/doi.org\/10.1007\/s11704-024-40231-1","DOI":"10.1007\/s11704-024-40231-1"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Wang, R., Yang, Z., Zhao, Z., Tong, X., Hong, Z., Qian, K.: LLM-based robot task planning with exceptional handling for general purpose service robots. arXiv preprint arXiv: 2405.15646 (2024)","DOI":"10.23919\/CCC63176.2024.10661966"},{"issue":"9","key":"21_CR27","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1038\/s41562-023-01659-w","volume":"7","author":"T Webb","year":"2023","unstructured":"Webb, T., Holyoak, K.J., Lu, H.: Emergent analogical reasoning in large language models. Nat. Hum. Behav. 7(9), 1526\u20131541 (2023)","journal-title":"Nat. Hum. Behav."},{"key":"21_CR28","unstructured":"Yao, S., et al.: ReAct: synergizing reasoning and acting in language models. In: The Eleventh International Conference on Learning Representations, ICLR (2023). https:\/\/openreview.net\/pdf?id=WE_vluYUL-X"},{"key":"21_CR29","unstructured":"Yasunaga, M., et al.: Large language models as analogical reasoners (2024)"},{"key":"21_CR30","unstructured":"Yu, J., He, R., Ying, R.: Thought propagation: an analogical approach to complex reasoning with large language models. arXiv preprint arXiv: 2310.03965 (2023)"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Yuan, S., Chen, J., Sun, C., Liang, J., Xiao, Y., Yang, D.: ANALOGYKB: unlocking analogical reasoning of language models with a million-scale knowledge base (2023)","DOI":"10.18653\/v1\/2024.acl-long.68"},{"key":"21_CR32","unstructured":"Zhou, P., et al.: Self-discover: large language models self-compose reasoning structures. arXiv preprint arXiv: 2402.03620 (2024)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8186-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:17:18Z","timestamp":1750155438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8186-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819681853","9789819681860"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8186-0_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"18 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","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":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}