{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:02:56Z","timestamp":1743134576852,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":12,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819607914"},{"type":"electronic","value":"9789819607921"}],"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\/978-981-96-0792-1_25","type":"book-chapter","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T09:27:34Z","timestamp":1737710854000},"page":"328-338","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Troubleshooting Task-Oriented Dialog Systems with\u00a0Large Language Models"],"prefix":"10.1007","author":[{"given":"Jiahao","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Fengda","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Caixia","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"Bai, J., et al.: Qwen technical report. https:\/\/doi.org\/10.48550\/arXiv.2309.16609, http:\/\/arxiv.org\/abs\/2309.16609","DOI":"10.48550\/arXiv.2309.16609"},{"key":"25_CR2","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol.\u00a033, pp. 1877\u20131901. Curran Associates, Inc. (2020). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"25_CR3","unstructured":"Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: QLoRA: efficient finetuning of quantized LLMs. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol.\u00a036, pp. 10088\u201310115. Curran Associates, Inc. (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/1feb87871436031bdc0f2beaa62a049b-Paper-Conference.pdf"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. https:\/\/doi.org\/10.48550\/arXiv.2106.09685, http:\/\/arxiv.org\/abs\/2106.09685","DOI":"10.48550\/arXiv.2106.09685"},{"key":"25_CR5","unstructured":"Meta AI: Introducing meta llama 3: The most capable openly available LLM to date. https:\/\/ai.meta.com\/blog\/meta-llama-3\/"},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"OpenAI: GPT-4 technical report. https:\/\/doi.org\/10.48550\/arXiv.2303.08774, http:\/\/arxiv.org\/abs\/2303.08774","DOI":"10.48550\/arXiv.2303.08774"},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Raghu, D., Agarwal, S., Joshi, S., Mausam: end-to-end learning of flowchart grounded task-oriented dialogs. https:\/\/doi.org\/10.48550\/arXiv.2109.07263, http:\/\/arxiv.org\/abs\/2109.07263","DOI":"10.48550\/arXiv.2109.07263"},{"key":"25_CR8","doi-asserted-by":"publisher","unstructured":"Touvron, H., et al.: LLaMA: open and efficient foundation language models. https:\/\/doi.org\/10.48550\/arXiv.2302.13971, http:\/\/arxiv.org\/abs\/2302.13971","DOI":"10.48550\/arXiv.2302.13971"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. https:\/\/doi.org\/10.48550\/arXiv.2307.09288, http:\/\/arxiv.org\/abs\/2307.09288","DOI":"10.48550\/arXiv.2307.09288"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Wei, J., et al.: Finetuned language models are zero-shot learners. https:\/\/doi.org\/10.48550\/arXiv.2109.01652, http:\/\/arxiv.org\/abs\/2109.01652","DOI":"10.48550\/arXiv.2109.01652"},{"key":"25_CR11","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems. vol.\u00a035, pp. 24824\u201324837. Curran Associates, Inc. (2022), https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf"},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Willard, B.T., Louf, R.: Efficient guided generation for large language models. https:\/\/doi.org\/10.48550\/arXiv.2307.09702, http:\/\/arxiv.org\/abs\/2307.09702","DOI":"10.48550\/arXiv.2307.09702"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0792-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T09:27:34Z","timestamp":1737710854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0792-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819607914","9789819607921"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0792-1_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 January 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":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"31 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icira2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}