{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:03:06Z","timestamp":1774400586903,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819794331","type":"print"},{"value":"9789819794348","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-981-97-9434-8_29","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"372-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Seal-Tools: Self-instruct Tool Learning Dataset for\u00a0Agent Tuning and\u00a0Detailed Benchmark"],"prefix":"10.1007","author":[{"given":"Mengsong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyuan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenliang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"29_CR1","unstructured":"Achiam, J., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"29_CR2","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"29_CR3","unstructured":"Dong, Q., et al.: A survey for in-context learning. arXiv preprint arXiv:2301.00234 (2022)"},{"key":"29_CR4","unstructured":"Gao, L., Chaudhary, A., Srinivasan, K., Hashimoto, K., Raman, K., Bendersky, M.: Ambiguity-aware in-context learning with large language models. arXiv preprint arXiv:2309.07900 (2023)"},{"key":"29_CR5","unstructured":"Hao, S., Liu, T., Wang, Z., Hu, Z.: Toolkengpt: augmenting frozen language models with massive tools via tool embeddings. arXiv e-prints pp. arXiv\u20132305 (2023)"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Hendel, R., Geva, M., Globerson, A.: In-context learning creates task vectors. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 9318\u20139333 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.624"},{"key":"29_CR7","unstructured":"Hsieh, C.Y., et al.: Tool documentation enables zero-shot tool-usage with large language models. arXiv preprint arXiv:2308.00675 (2023)"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Jin, Q., Yang, Y., Chen, Q., Lu, Z.: Genegpt: augmenting large language models with domain tools for improved access to biomedical information. ArXiv pp. arXiv\u20132304 (2023)","DOI":"10.1093\/bioinformatics\/btae075"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Li, M., et al.: Api-bank: a comprehensive benchmark for tool-augmented llms. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 3102\u20133116 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.187"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Liang, Y., et\u00a0al.: Taskmatrix. ai: completing tasks by connecting foundation models with millions of apis. arXiv e-prints pp. arXiv\u20132303 (2023)","DOI":"10.34133\/icomputing.0063"},{"key":"29_CR11","unstructured":"Patil, S.G., Zhang, T., Wang, X., Gonzalez, J.E.: Gorilla: large language model connected with massive apis. arXiv e-prints pp. arXiv\u20132305 (2023)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Qian, C., Han, C., Fung, Y., Qin, Y., Liu, Z., Ji, H.: Creator: tool creation for disentangling abstract and concrete reasoning of large language models. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 6922\u20136939 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.462"},{"key":"29_CR13","unstructured":"Qin, Y., et\u00a0al.: Toolllm: facilitating large language models to master 16000+ real-world apis. arXiv e-prints pp. arXiv\u20132307 (2023)"},{"key":"29_CR14","unstructured":"Schick, T., et al.: Toolformer: language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 (2023)"},{"key":"29_CR15","unstructured":"Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y.: Hugginggpt: solving ai tasks with chatgpt and its friends in huggingface. arXiv e-prints pp. arXiv\u20132303 (2023)"},{"key":"29_CR16","unstructured":"Tang, Q., Deng, Z., Lin, H., Han, X., Liang, Q., Sun, L.: Toolalpaca: generalized tool learning for language models with 3000 simulated cases. arXiv e-prints pp. arXiv\u20132306 (2023)"},{"key":"29_CR17","unstructured":"Wei, Y., Wang, Z., Liu, J., Ding, Y., Zhang, L.: Magicoder: source code is all you need. arXiv e-prints pp. arXiv\u20132312 (2023)"},{"issue":"5","key":"29_CR18","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1109\/JAS.2023.123618","volume":"10","author":"T Wu","year":"2023","unstructured":"Wu, T., et al.: A brief overview of chatgpt: the history, status quo and potential future development. IEEE\/CAA J. Automatica Sinica 10(5), 1122\u20131136 (2023)","journal-title":"IEEE\/CAA J. Automatica Sinica"},{"key":"29_CR19","unstructured":"Xu, Q., Hong, F., Li, B., Hu, C., Chen, Z., Zhang, J.: On the tool manipulation capability of open-source large language models. arXiv e-prints pp. arXiv\u20132305 (2023)"},{"key":"29_CR20","unstructured":"Yang, J., Ma, S., Wei, F.: Auto-icl: in-context learning without human supervision. arXiv preprint arXiv:2311.09263 (2023)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-9434-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:38:32Z","timestamp":1730385512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-9434-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9789819794331","9789819794348"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-9434-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 November 2024","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 interest"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"2 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}