{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T20:03:02Z","timestamp":1778702582945,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T00:00:00Z","timestamp":1722297600000},"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":["J Intell Inf Syst"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s10844-024-00869-6","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T16:02:28Z","timestamp":1722355348000},"page":"1725-1747","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SESAME - self-supervised framework for extractive question answering over document collections"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9095-0266","authenticated-orcid":false,"given":"Vitor A.","family":"Batista","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9351-7647","authenticated-orcid":false,"given":"Diogo S. M.","family":"Gomes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7828-0124","authenticated-orcid":false,"given":"Alexandre","family":"Evsukoff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"issue":"3","key":"869_CR1","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s10844-022-00724-6","volume":"59","author":"Z Abbasiantaeb","year":"2022","unstructured":"Abbasiantaeb, Z., & Momtazi, S. (2022). Entity-aware answer sentence selection for question answering with transformer-based language models. Journal of Intelligent Information Systems, 59(3), 755\u2013777. https:\/\/doi.org\/10.1007\/s10844-022-00724-6","journal-title":"Journal of Intelligent Information Systems"},{"key":"869_CR2","doi-asserted-by":"publisher","unstructured":"Almazrouei, E., Alobeidli, H., & Alshamsi, A., et\u00a0al. (2023). The falcon series of open language models. https:\/\/doi.org\/10.48550\/arXiv.2311.16867","DOI":"10.48550\/arXiv.2311.16867"},{"key":"869_CR3","doi-asserted-by":"publisher","unstructured":"Asai, A., Wu, Z., & Wang, Y., et\u00a0al. (2023). Self-RAG: Self-reflective retrieval augmented generation. In: NeurIPS 2023 workshop on instruction tuning and instruction following. https:\/\/doi.org\/10.48550\/arXiv.2310.11511","DOI":"10.48550\/arXiv.2310.11511"},{"key":"869_CR4","doi-asserted-by":"publisher","unstructured":"Assem, H., Sarkar, R., & Dutta, S. (2021). Qasar: Self-supervised learning framework for extractive question answering. In: 2021 IEEE international conference on big data (Big data) (pp. 1797\u20131808). https:\/\/doi.org\/10.1109\/BigData52589.2021.9671570","DOI":"10.1109\/BigData52589.2021.9671570"},{"key":"869_CR5","doi-asserted-by":"publisher","unstructured":"Banerjee, P., Gokhale, T., & Baral, C. (2021). Self-supervised test-time learning for reading comprehension. In: K. Toutanova, A. Rumshisky, & L. Zettlemoyer, et\u00a0al (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 1200\u20131211). Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.95","DOI":"10.18653\/v1\/2021.naacl-main.95"},{"key":"869_CR6","doi-asserted-by":"publisher","unstructured":"Brill, E., Dumais, S., & Banko, M. (2002). An analysis of the askmsr question-answering system. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002) (pp. 257\u2013264). https:\/\/doi.org\/10.3115\/1118693.1118726","DOI":"10.3115\/1118693.1118726"},{"key":"869_CR7","doi-asserted-by":"publisher","unstructured":"Brown, T., Mann, B., & Ryder, N., et\u00a0al. (2020). Language models are few-shot learners. In: Advances in neural information processing systems (pp. 1877\u20131901). https:\/\/doi.org\/10.48550\/arXiv.2005.14165","DOI":"10.48550\/arXiv.2005.14165"},{"key":"869_CR8","doi-asserted-by":"publisher","unstructured":"Carmo, V. (2022). A framework for closed domain question answering systems in the low data regime. Master\u2019s thesis, Universidade de S\u00e3o Paulo. https:\/\/doi.org\/10.11606\/D.3.2022.tde-24052023-152815","DOI":"10.11606\/D.3.2022.tde-24052023-152815"},{"key":"869_CR9","doi-asserted-by":"publisher","DOI":"10.1145\/3641289","author":"Y Chang","year":"2024","unstructured":"Chang, Y., Wang, X., Wang, J., et al. (2024). A survey on evaluation of large language models. ACM Trans Intell Syst Technol. https:\/\/doi.org\/10.1145\/3641289","journal-title":"ACM Trans Intell Syst Technol"},{"key":"869_CR10","doi-asserted-by":"publisher","unstructured":"Chen, D., & Yih, W. t. (2020). Open-domain question answering. In: Proceedings of the 58th annual meeting of the association for computational linguistics: tutorial abstracts (pp. 34\u201337). https:\/\/doi.org\/10.18653\/v1\/2020.acl-tutorials.8","DOI":"10.18653\/v1\/2020.acl-tutorials.8"},{"key":"869_CR11","doi-asserted-by":"publisher","unstructured":"Chen, J., Lin, H., & Han, X., et\u00a0al. (2023). Benchmarking large language models in retrieval-augmented generation. arxiv:2309.01431. https:\/\/doi.org\/10.48550\/arXiv.2309.01431","DOI":"10.48550\/arXiv.2309.01431"},{"key":"869_CR12","doi-asserted-by":"publisher","unstructured":"Chuang, Y. S., Fang, W., & Li, S. W., et\u00a0al. (2023). Expand, rerank, and retrieve: Query reranking for open-domain question answering. In: Annual meeting of the association for computational linguistics (pp. 12131\u201312147). Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.768","DOI":"10.18653\/v1\/2023.findings-acl.768"},{"key":"869_CR13","doi-asserted-by":"publisher","unstructured":"Cormack, G. V., Clarke, C. L. A., & Buettcher, S. (2009). Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval (pp. 758\u2013759). Association for Computing Machinery, New York, NY, USA, SIGIR \u201909. https:\/\/doi.org\/10.1145\/1571941.1572114","DOI":"10.1145\/1571941.1572114"},{"key":"869_CR14","doi-asserted-by":"publisher","unstructured":"Dettmers, T., Pagnoni, A., & Holtzman, A., et\u00a0al. (2023). Qlora: Efficient finetuning of quantized llms. https:\/\/doi.org\/10.48550\/arXiv.2305.14314","DOI":"10.48550\/arXiv.2305.14314"},{"key":"869_CR15","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M. W., & Lee, K., et\u00a0al. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In: J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol. 1 (Long and short papers) (pp. 4171\u20134186). Association for Computational Linguistics, Minneapolis, Minnesota. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"869_CR16","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10844-019-00584-7","volume":"55","author":"E Dimitrakis","year":"2019","unstructured":"Dimitrakis, E., Sgontzos, K., & Tzitzikas, Y. (2019). A survey on question answering systems over linked data and documents. Journal of Intelligent Information Systems, 55, 233\u2013259. https:\/\/doi.org\/10.1007\/s10844-019-00584-7","journal-title":"Journal of Intelligent Information Systems"},{"issue":"3","key":"869_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1609\/aimag.v31i3.2303","volume":"31","author":"D Ferrucci","year":"2010","unstructured":"Ferrucci, D., Brown, E., Chu-Carroll, J., et al. (2010). Building watson: An overview of the deepqa project. AI Magazine, 31(3), 59\u201379. https:\/\/doi.org\/10.1609\/aimag.v31i3.2303","journal-title":"AI Magazine"},{"key":"869_CR18","doi-asserted-by":"publisher","unstructured":"Fisch, A., Talmor, A., & Jia, R., et\u00a0al. (2019). MRQA 2019 shared task: Evaluating generalization in reading comprehension. In: A. Fisch, A. Talmor, & R. Jia, et\u00a0al. (Eds.), Proceedings of the 2nd workshop on machine reading for question answering (pp. 1\u201313). Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-5801","DOI":"10.18653\/v1\/D19-5801"},{"key":"869_CR19","doi-asserted-by":"publisher","unstructured":"Gao, Y., Xiong, Y., & Gao, X., et\u00a0al. (2024). Retrieval-augmented generation for large language models: A survey. https:\/\/doi.org\/10.48550\/arXiv.2312.10997","DOI":"10.48550\/arXiv.2312.10997"},{"key":"869_CR20","doi-asserted-by":"publisher","unstructured":"Green, B. F., Wolf, A. K., & Chomsky, C., et\u00a0al. (1961). Baseball: An automatic question-answerer. In: Papers presented at the May 9-11, 1961, Western Joint IRE-AIEE-ACM computer conference (pp. 219\u2013224). Association for Computing Machinery, New York, USA, IRE-AIEE-ACM \u201961 (Western). https:\/\/doi.org\/10.1145\/1460690.1460714","DOI":"10.1145\/1460690.1460714"},{"key":"869_CR21","doi-asserted-by":"publisher","unstructured":"Hu, E. J., Shen, Y., & Wallis, P., et\u00a0al. (2022). LoRA: Low-rank adaptation of large language models. In: International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.2106.09685","DOI":"10.48550\/arXiv.2106.09685"},{"key":"869_CR22","doi-asserted-by":"publisher","unstructured":"Jiang, A. Q., Sablayrolles, A., & Roux, A., et\u00a0al. (2024). Mixtral of experts. arxiv:2401.04088. https:\/\/doi.org\/10.48550\/arXiv.2401.04088","DOI":"10.48550\/arXiv.2401.04088"},{"key":"869_CR23","doi-asserted-by":"publisher","unstructured":"Joshi, M., Choi, E., & Weld, D., et\u00a0al. (2017). TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In: R. Barzilay, & M. Y. Kan (Eds.), Proceedings of the 55th annual meeting of the association for computational linguistics (vol. 1: Long papers) (pp. 1601\u20131611). Association for Computational Linguistics, Vancouver, Canada. https:\/\/doi.org\/10.18653\/v1\/P17-1147","DOI":"10.18653\/v1\/P17-1147"},{"key":"869_CR24","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi, M., Chen, D., Liu, Y., et al. (2020). SpanBERT: Improving pre-training by representing and predicting spans. Transactions of the Association for Computational Linguistics, 8, 64\u201377. https:\/\/doi.org\/10.1162\/tacl_a_00300","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"869_CR25","doi-asserted-by":"publisher","unstructured":"Jurafsky, D., & Martin, J. H. (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 1st edn. Prentice Hall PTR, USA. https:\/\/doi.org\/10.1162\/089120100750105975","DOI":"10.1162\/089120100750105975"},{"key":"869_CR26","doi-asserted-by":"publisher","unstructured":"Karpukhin, V., Oguz, B., & Min, S., et\u00a0al. (2020). Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 6769\u20136781). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"issue":"24","key":"869_CR27","doi-asserted-by":"publisher","first-page":"5412","DOI":"10.1016\/j.ins.2011.07.047","volume":"181","author":"O Kolomiyets","year":"2011","unstructured":"Kolomiyets, O., & Moens, M. F. (2011). A survey on question answering technology from an information retrieval perspective. Information Sciences, 181(24), 5412\u20135434. https:\/\/doi.org\/10.1016\/j.ins.2011.07.047","journal-title":"Information Sciences"},{"key":"869_CR28","doi-asserted-by":"publisher","unstructured":"Lee, H., Yoon, S., & Dernoncourt, F., et\u00a0al. (2021). KPQA: A metric for generative question answering using keyphrase weights. In: K. Toutanova, A. Rumshisky, & L. Zettlemoyer, et\u00a0al. (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 2105\u20132115). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.170","DOI":"10.18653\/v1\/2021.naacl-main.170"},{"key":"869_CR29","doi-asserted-by":"publisher","unstructured":"Lialin, V., Muckatira, S., & Shivagunde, N., et\u00a0al. (2023). ReloRA: High-rank training through low-rank updates. In: Workshop on advancing neural network training: computational efficiency, scalability, and resource optimization (WANT@NeurIPS 2023). https:\/\/doi.org\/10.48550\/arXiv.2307.05695","DOI":"10.48550\/arXiv.2307.05695"},{"key":"869_CR30","doi-asserted-by":"publisher","unstructured":"Ling, C., Zhao, X., & Lu, J., et\u00a0al. (2023). Domain specialization as the key to make large language models disruptive: A comprehensive survey. https:\/\/doi.org\/10.48550\/arXiv.2305.18703","DOI":"10.48550\/arXiv.2305.18703"},{"key":"869_CR31","doi-asserted-by":"publisher","unstructured":"Lu, J., Li, W., & Wang, Q., et\u00a0al. (2020). Research on data quality control of crowdsourcing annotation: A survey. In: 2020 IEEE Intl Conf on dependable, autonomic and secure computing, Intl Conf on pervasive intelligence and computing, Intl Conf on cloud and big data computing, Intl Conf on cyber science and technology congress (DASC\/PiCom\/CBDCom\/CyberSciTech) (pp. 201\u2013208). https:\/\/doi.org\/10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00044","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech49142.2020.00044"},{"key":"869_CR32","doi-asserted-by":"publisher","unstructured":"Lu, J., Hall, K., & Ma, J., et\u00a0al. (2022). Hyrr: Hybrid infused reranking for passage retrieval. https:\/\/doi.org\/10.48550\/arXiv.2212.10528","DOI":"10.48550\/arXiv.2212.10528"},{"key":"869_CR33","doi-asserted-by":"publisher","unstructured":"Luo, M., Jain, S., & Gupta, A., et\u00a0al. (2023). A study on the efficiency and generalization of light hybrid retrievers. In: A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st annual meeting of the association for computational linguistics (vol 2: Short papers) (pp. 1617\u20131626). Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-short.139","DOI":"10.18653\/v1\/2023.acl-short.139"},{"key":"869_CR34","doi-asserted-by":"publisher","unstructured":"Mallen, A., Asai, A., & Zhong, V., et\u00a0al. (2023). When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. In: A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st annual meeting of the association for computational linguistics (vol 1: Long papers) (pp. 9802\u20139822). Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.546","DOI":"10.18653\/v1\/2023.acl-long.546"},{"key":"869_CR35","doi-asserted-by":"publisher","unstructured":"Mao, Y., He, P., & Liu, X., et\u00a0al. (2021). Generation-augmented retrieval for open-domain question answering. In: C. Zong, F. Xia, & W. Li, et\u00a0al (Eds.), Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol 1: Long papers) (pp. 4089\u20134100). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.316","DOI":"10.18653\/v1\/2021.acl-long.316"},{"key":"869_CR36","doi-asserted-by":"publisher","unstructured":"Pan, L., Chen, W., & Xiong, W., et\u00a0al. (2021). Unsupervised multi-hop question answering by question generation. In: K. Toutanova, A. Rumshisky, & L. Zettlemoyer, et\u00a0al (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 5866\u20135880). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.469","DOI":"10.18653\/v1\/2021.naacl-main.469"},{"key":"869_CR37","doi-asserted-by":"publisher","unstructured":"Puri, R., Spring, R., & Shoeybi, M., et\u00a0al. (2020). Training question answering models from synthetic data. In: B. Webber, T. Cohn, & Y. He, et\u00a0al (Eds.), Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 5811\u20135826). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.468","DOI":"10.18653\/v1\/2020.emnlp-main.468"},{"key":"869_CR38","doi-asserted-by":"publisher","unstructured":"Qu, Y., Ding, Y., & Liu, J., et\u00a0al. (2021). RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. In: K. Toutanova, A. Rumshisky, & L. Zettlemoyer, et\u00a0al (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 5835\u20135847). Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.466","DOI":"10.18653\/v1\/2021.naacl-main.466"},{"issue":"140","key":"869_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1910.10683","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., Roberts, A., et al. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(140), 1\u201367. https:\/\/doi.org\/10.48550\/arXiv.1910.10683","journal-title":"The Journal of Machine Learning Research"},{"key":"869_CR40","doi-asserted-by":"publisher","unstructured":"Rajpurkar, P., Jia, R., & Liang, P. (2018). Know what you don\u2019t know: Unanswerable questions for SQuAD. In: I. Gurevych, & Y. Miyao (Eds.), Proceedings of the 56th annual meeting of the association for computational linguistics (vol 2: Short papers) (pp. 784\u2013789). Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-2124","DOI":"10.18653\/v1\/P18-2124"},{"key":"869_CR41","doi-asserted-by":"publisher","unstructured":"Rawte, V., Priya, P., & Tonmoy, S. T. I., et\u00a0al. (2023). Exploring the relationship between llm hallucinations and prompt linguistic nuances: Readability, formality, and concreteness. arxiv:2309.11064. https:\/\/doi.org\/10.48550\/arXiv.2309.11064","DOI":"10.48550\/arXiv.2309.11064"},{"key":"869_CR42","doi-asserted-by":"publisher","unstructured":"Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. https:\/\/doi.org\/10.48550\/arXiv.1908.10084","DOI":"10.48550\/arXiv.1908.10084"},{"key":"869_CR43","doi-asserted-by":"publisher","unstructured":"Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends$${\\circledR} $$in Information Retrieval, 3(4), 333\u2013389. https:\/\/doi.org\/10.1561\/1500000019","DOI":"10.1561\/1500000019"},{"key":"869_CR44","doi-asserted-by":"publisher","unstructured":"Robinson, J. D., Chuang, C. Y., & Sra, S., et\u00a0al. (2021). Contrastive learning with hard negative samples. In: International conference on learning representations. https:\/\/doi.org\/10.48550\/arXiv.2010.04592","DOI":"10.48550\/arXiv.2010.04592"},{"key":"869_CR45","doi-asserted-by":"publisher","unstructured":"Sayama, H. F., Araujo, A. V., & Fernandes, E. R. (2019). Faquad: Reading comprehension dataset in the domain of brazilian higher education. In: 2019 8th Brazilian conference on intelligent systems (BRACIS) (pp. 443\u2013448). https:\/\/doi.org\/10.1109\/BRACIS.2019.00084","DOI":"10.1109\/BRACIS.2019.00084"},{"key":"869_CR46","doi-asserted-by":"publisher","unstructured":"Sharir, O., Peleg, B., & Shoham, Y. (2020). The cost of training nlp models: A concise overview. https:\/\/doi.org\/10.48550\/arXiv.2004.08900","DOI":"10.48550\/arXiv.2004.08900"},{"issue":"1","key":"869_CR47","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1108\/eb026526","volume":"28","author":"K Sparck Jones","year":"1972","unstructured":"Sparck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28(1), 11\u201321. https:\/\/doi.org\/10.1108\/eb026526","journal-title":"Journal of Documentation"},{"key":"869_CR48","doi-asserted-by":"publisher","unstructured":"Touvron, H., Lavril, T., & Izacard, G., et\u00a0al. (2023). Llama: Open and efficient foundation language models. https:\/\/doi.org\/10.48550\/arXiv.2302.13971","DOI":"10.48550\/arXiv.2302.13971"},{"key":"869_CR49","doi-asserted-by":"publisher","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et\u00a0al. (2017). Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems, vol\u00a030 (pp. p 6000\u20136010). Curran Associates, Inc., Red Hook, NY, USA. https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"869_CR50","doi-asserted-by":"publisher","unstructured":"Voorhees, E. M., & Tice, D. M. (2000). The TREC-8 question answering track. In: M. Gavrilidou, G. Carayannis, & S. Markantonatou, et\u00a0al. (Eds.), Proceedings of the second international conference on language resources and evaluation (LREC\u201900). European Language Resources Association (ELRA), Athens, Greece. https:\/\/doi.org\/10.1017\/S1351324901002789","DOI":"10.1017\/S1351324901002789"},{"issue":"5","key":"869_CR51","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1109\/JAS.2023.123618","volume":"10","author":"T Wu","year":"2023","unstructured":"Wu, T., He, S., Liu, J., et al. (2023). A brief overview of chatgpt: The history, status quo and potential future development. IEEE\/CAA Journal of Automatica Sinica, 10(5), 1122\u20131136. https:\/\/doi.org\/10.1109\/JAS.2023.123618","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"869_CR52","doi-asserted-by":"publisher","unstructured":"Zeng, C., Li, S., & Li, Q., et\u00a0al. (2020). A survey on machine reading comprehension\u2014tasks, evaluation metrics and benchmark datasets. Applied Sciences, 10(21). https:\/\/doi.org\/10.3390\/app10217640","DOI":"10.3390\/app10217640"},{"key":"869_CR53","doi-asserted-by":"publisher","unstructured":"Zerveas, G., Rekabsaz, N., & Cohen, D., et\u00a0al. (2022). CODER: An efficient framework for improving retrieval through COntextual document embedding reranking. In: Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 conference on empirical methods in natural language processing (pp. 10626\u201310644). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. https:\/\/doi.org\/10.18653\/v1\/2022.emnlp-main.727","DOI":"10.18653\/v1\/2022.emnlp-main.727"},{"key":"869_CR54","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Chen, S., & Xu, D., et\u00a0al. (2023). A survey for efficient open domain question answering. In: A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st annual meeting of the association for computational linguistics (vol 1: Long papers) (pp. 14447\u201314465). Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.808","DOI":"10.18653\/v1\/2023.acl-long.808"},{"key":"869_CR55","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Long, D., & Xu, G., et\u00a0al. (2022). Hlatr: Enhance multi-stage text retrieval with hybrid list aware transformer reranking. arxiv:2205.10569. https:\/\/doi.org\/10.48550\/arXiv.2205.10569","DOI":"10.48550\/arXiv.2205.10569"},{"key":"869_CR56","doi-asserted-by":"publisher","unstructured":"Zhu, F., Lei, W., & Wang, C., et\u00a0al. (2021). Retrieving and reading: A comprehensive survey on open-domain question answering. https:\/\/doi.org\/10.48550\/arXiv.2101.00774","DOI":"10.48550\/arXiv.2101.00774"},{"issue":"3","key":"869_CR57","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1007\/S10844-023-00800-5","volume":"61","author":"P Zhu","year":"2023","unstructured":"Zhu, P., Yuan, Y., & Chen, L. (2023). Electra-based graph network model for multi-hop question answering. Journal of Intelligent Information Systems, 61(3), 819\u2013834. https:\/\/doi.org\/10.1007\/S10844-023-00800-5","journal-title":"Journal of Intelligent Information Systems"}],"container-title":["Journal of Intelligent Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-024-00869-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10844-024-00869-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10844-024-00869-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T11:53:08Z","timestamp":1737719588000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10844-024-00869-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,30]]},"references-count":57,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["869"],"URL":"https:\/\/doi.org\/10.1007\/s10844-024-00869-6","relation":{},"ISSN":["0925-9902","1573-7675"],"issn-type":[{"value":"0925-9902","type":"print"},{"value":"1573-7675","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,30]]},"assertion":[{"value":"15 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Ethics"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}