{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:12:55Z","timestamp":1778346775492,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2023A1515011370"],"award-info":[{"award-number":["2023A1515011370"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32371114"],"award-info":[{"award-number":["32371114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Characteristic Innovation Projects of Guangdong Colleges and Universities","award":["2018KTSCX049"],"award-info":[{"award-number":["2018KTSCX049"]}]},{"name":"Guangdong Provincial Key Laboratory","award":["2023B1212060076"],"award-info":[{"award-number":["2023B1212060076"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s13042-025-02711-z","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:31:20Z","timestamp":1750221080000},"page":"8079-8101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Evaluating zero-shot multilingual aspect-based sentiment analysis with large language models"],"prefix":"10.1007","volume":"16","author":[{"given":"Chengyan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyuan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"2711_CR1","doi-asserted-by":"publisher","unstructured":"Liu B (2012) Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. https:\/\/doi.org\/10.2200\/S00416ED1V01Y201204HLT016","DOI":"10.2200\/S00416ED1V01Y201204HLT016"},{"key":"2711_CR2","doi-asserted-by":"publisher","unstructured":"Ng S, Rahman N, Ang I, Sridharan S, Ramachandran S, Wang D, Khoo A, Tan C, Feng M Toh S et al Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore. BMJ Open Jan 06; 10(1):e031622. https:\/\/doi.org\/10.1136\/bmjopen-2019-031622","DOI":"10.1136\/bmjopen-2019-031622"},{"issue":"1","key":"2711_CR3","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s12860-021-00358-6","volume":"22","author":"M Zhu","year":"2021","unstructured":"Zhu M, Wang DD, Yan H (2021) Genotype-determined egfr-rtk heterodimerization and its effects on drug resistance in lung cancer treatment revealed by molecular dynamics simulations. BMC Mol Cell Biol 22(1):34","journal-title":"BMC Mol Cell Biol"},{"issue":"4","key":"2711_CR4","doi-asserted-by":"publisher","first-page":"10933","DOI":"10.2196\/10933","volume":"6","author":"N Rahman","year":"2018","unstructured":"Rahman N, Wang DD, Ng SH-X, Ramachandran S, Sridharan S, Khoo A, Tan CS, Goh W-P, Tan XQ et al (2018) Processing of electronic medical records for health services research in an academic medical center: methods and validation. JMIR Med Inform 6(4):10933","journal-title":"JMIR Med Inform"},{"issue":"15","key":"2711_CR5","doi-asserted-by":"publisher","first-page":"13543","DOI":"10.1609\/aaai.v35i15.17597","volume":"35","author":"Y Mao","year":"2022","unstructured":"Mao Y, Shen Y, Yu C, Cai L (2022) A joint training dual-mrc framework for aspect based sentiment analysis. Proc AAAI Conf Artif Intell 35(15):13543\u201313551. https:\/\/doi.org\/10.1609\/aaai.v35i15.17597","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2711_CR6","doi-asserted-by":"publisher","unstructured":"Zhang W, Li X, Deng Y, Bing L, Lam W (2021) Towards generative aspect-based sentiment analysis. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 504\u2013510. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-short.64","DOI":"10.18653\/v1\/2021.acl-short.64"},{"key":"2711_CR7","doi-asserted-by":"crossref","unstructured":"Tran V, Matsui T (2024) Improving llm prompting with ensemble of instructions: A case study on sentiment analysis. JSAI International Symposium on Artificial Intelligence, Springer, pp 299\u2013305","DOI":"10.1007\/978-981-97-3076-6_21"},{"key":"2711_CR8","doi-asserted-by":"crossref","unstructured":"Schaik TA, Pugh B (2024) A field guide to automatic evaluation of llm-generated summaries. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2832\u20132836","DOI":"10.1145\/3626772.3661346"},{"key":"2711_CR9","volume":"6","author":"KI Roumeliotis","year":"2024","unstructured":"Roumeliotis KI, Tselikas ND, Nasiopoulos DK (2024) Llms in e-commerce: a comparative analysis of gpt and llama models in product review evaluation. Nat Lang Proc J 6:100056","journal-title":"Nat Lang Proc J"},{"key":"2711_CR10","doi-asserted-by":"publisher","unstructured":"Xie T, Li Q, Zhang J, Zhang Y, Liu Z, Wang H (2023) Empirical study of zero-shot NER with ChatGPT. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 7935\u20137956. Association for Computational Linguistics, Singapore. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.493","DOI":"10.18653\/v1\/2023.emnlp-main.493"},{"key":"2711_CR11","doi-asserted-by":"crossref","unstructured":"Ma B, Nie E, Yuan S, Schmid H, F\u00e4rber M, Kreuter F, Schuetze H (2024) ToPro: Token-level prompt decomposition for cross-lingual sequence labeling tasks. In: Graham, Y., Purver, M. (eds.) Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2685\u20132702. Association for Computational Linguistics, St. Julian\u2019s, Malta. https:\/\/aclanthology.org\/2024.eacl-long.164","DOI":"10.18653\/v1\/2024.eacl-long.164"},{"key":"2711_CR12","unstructured":"Nie E, Yuan S, Ma B, Schmid H, F\u00e4rber M, Kreuter F, Sch\u00fctze H (2024) Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models. https:\/\/arxiv.org\/abs\/2402.18397"},{"key":"2711_CR13","doi-asserted-by":"publisher","unstructured":"Zhang W, Deng Y, Liu B, Pan S, Bing L (2024) Sentiment analysis in the era of large language models: A reality check. In: Duh, K., Gomez, H., Bethard, S. (eds.) Findings of the Association for Computational Linguistics: NAACL 2024, pp. 3881\u20133906. Association for Computational Linguistics, Mexico City, Mexico. https:\/\/doi.org\/10.18653\/v1\/2024.findings-naacl.246","DOI":"10.18653\/v1\/2024.findings-naacl.246"},{"key":"2711_CR14","doi-asserted-by":"publisher","unstructured":"Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O, Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jim\u00e9nez-Zafra SM, Eryi\u011fit G (2016) SemEval-2016 task 5: Aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016, (ed) Bethard, S., Carpuat, M., Cer, D., Jurgens, D., Nakov, P., Zesch, T. Association for Computational Linguistics, San Diego, California, pp 19\u201330. https:\/\/doi.org\/10.18653\/v1\/S16-1002","DOI":"10.18653\/v1\/S16-1002"},{"key":"2711_CR15","doi-asserted-by":"crossref","unstructured":"Lin Z, Jin X, Xu X, Wang W, Cheng X, Wang Y (2014) A cross-lingual joint aspect\/sentiment model for sentiment analysis. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1089\u20131098","DOI":"10.1145\/2661829.2662019"},{"key":"2711_CR16","doi-asserted-by":"publisher","unstructured":"Klinger R, Cimiano P (2015) Instance selection improves cross-lingual model training for fine-grained sentiment analysis. In: Proceedings of the Nineteenth Conference on Computational Natural Language Learning, pp. 153\u2013163. Association for Computational Linguistics, Beijing, China. https:\/\/doi.org\/10.18653\/v1\/K15-1016 . https:\/\/aclanthology.org\/K15-1016","DOI":"10.18653\/v1\/K15-1016"},{"key":"2711_CR17","doi-asserted-by":"publisher","unstructured":"Lambert P (2015) Aspect-level cross-lingual sentiment classification with constrained SMT. In: Zong, C., Strube, M. (eds.) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 781\u2013787. Association for Computational Linguistics, Beijing, China. https:\/\/doi.org\/10.3115\/v1\/P15-2128 . https:\/\/aclanthology.org\/P15-2128","DOI":"10.3115\/v1\/P15-2128"},{"key":"2711_CR18","unstructured":"Barnes J, Lambert P, Badia T (2016) Exploring distributional representations and machine translation for aspect-based cross-lingual sentiment classification. In: Matsumoto, Y., Prasad, R. (eds.) Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1613\u20131623. The COLING 2016 Organizing Committee, Osaka, Japan. https:\/\/aclanthology.org\/C16-1152"},{"key":"2711_CR19","doi-asserted-by":"publisher","unstructured":"Phan KT-K, Ngoc\u00a0Hao D, Thin DV, Luu-Thuy\u00a0Nguyen N (2021) Exploring zero-shot cross-lingual aspect-based sentiment analysis using pre-trained multilingual language models. In: 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1\u20136. https:\/\/doi.org\/10.1109\/MAPR53640.2021.9585242","DOI":"10.1109\/MAPR53640.2021.9585242"},{"key":"2711_CR20","doi-asserted-by":"publisher","unstructured":"Xu H, Liu B, Shu L, Yu P (2019) BERT post-training for review reading comprehension and aspect-based sentiment analysis. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 2324\u20132335. Association for Computational Linguistics, Minneapolis, Minnesota . https:\/\/doi.org\/10.18653\/v1\/N19-1242 . https:\/\/aclanthology.org\/N19-1242","DOI":"10.18653\/v1\/N19-1242"},{"key":"2711_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107220","volume":"227","author":"A Zhao","year":"2021","unstructured":"Zhao A, Yu Y (2021) Knowledge-enabled bert for aspect-based sentiment analysis. Knowl-Based Syst 227:107220. https:\/\/doi.org\/10.1016\/j.knosys.2021.107220","journal-title":"Knowl-Based Syst"},{"key":"2711_CR22","doi-asserted-by":"publisher","unstructured":"Zhang W, He R, Peng H, Bing L, Lam W (2021) Cross-lingual aspect-based sentiment analysis with aspect term code-switching. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 9220\u20139230. Association for Computational Linguistics, ???. https:\/\/doi.org\/10.18653\/V1\/2021.EMNLP-MAIN.727 . https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.727","DOI":"10.18653\/V1\/2021.EMNLP-MAIN.727"},{"key":"2711_CR23","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TASLP.2023.3297964","volume":"31","author":"N Lin","year":"2023","unstructured":"Lin N, Fu Y, Lin X, Zhou D, Yang A, Jiang S (2023) Cl-xabsa: Contrastive learning for cross-lingual aspect-based sentiment analysis. IEEE\/ACM Trans Audio Speech Lang Process 31:2935\u20132946. https:\/\/doi.org\/10.1109\/TASLP.2023.3297964","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"2711_CR24","doi-asserted-by":"publisher","unstructured":"Zhang W, He R, Peng H, Bing L, Lam W (2021) Cross-lingual aspect-based sentiment analysis with aspect term code-switching. In: Moens, M.-F., Huang, X., Specia, L., Yih, S.W.-t. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9220\u20139230. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.727 . https:\/\/aclanthology.org\/2021.emnlp-main.727","DOI":"10.18653\/v1\/2021.emnlp-main.727"},{"issue":"6","key":"2711_CR25","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.23919\/cje.2021.00.339","volume":"32","author":"Z Jun","year":"2023","unstructured":"Jun Z, Longlong Q, Fanfan S, Yueshun H, Hai T, Yanxiang H (2023) Rating text classification with weighted negative supervision on classifier layer. Chin J Electron 32(6):1304\u20131318","journal-title":"Chin J Electron"},{"issue":"4","key":"2711_CR26","doi-asserted-by":"publisher","first-page":"854","DOI":"10.23919\/cje.2022.00.279","volume":"32","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Li J, Xin Y, Zhao X, Liu Y (2023) A model for chinese named entity recognition based on global pointer and adversarial learning. Chin J Electron 32(4):854\u2013867","journal-title":"Chin J Electron"},{"key":"2711_CR27","unstructured":"Wang S, Sun X, Li X, Ouyang R, Wu F, Zhang, T, Li J, Wang G (2023) Gpt-ner: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428"},{"key":"2711_CR28","unstructured":"Simmering PF, Huoviala P (2023) Large language models for aspect-based sentiment analysis. https:\/\/arxiv.org\/abs\/2310.18025"},{"key":"2711_CR29","unstructured":"Wang X, Wei J, Schuurmans D, Le QV, Chi EH, Narang S, Chowdhery A, Zhou D (2023) Self-Consistency Improves Chain of Thought Reasoning in Language Models. https:\/\/openreview.net\/forum?id=1PL1NIMMrw"},{"key":"2711_CR30","unstructured":"Wang Q, Xu H, Ding K, Liang B, Xu R (2024) In-context example retrieval from multi-perspectives for few-shot aspect-based sentiment analysis. In: Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (eds.) Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp. 8975\u20138985. ELRA and ICCL, Torino, Italia. https:\/\/aclanthology.org\/2024.lrec-main.786"},{"key":"2711_CR31","unstructured":"Zhong Q, Ding L, Liu J, Du B, Tao D Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert"},{"key":"2711_CR32","doi-asserted-by":"crossref","unstructured":"Wang Z, Xie Q, Ding Z, Feng Y, Xia R (2023) Is chatgpt a good sentiment analyzer? a preliminary study","DOI":"10.18653\/v1\/2023.newsum-1.1"},{"key":"2711_CR33","unstructured":"Han R, Peng T, Yang C, Wang B, Liu L, Wan X Is information extraction solved by chatgpt? an analysis of performance, evaluation criteria, robustness and errors"},{"key":"2711_CR34","unstructured":"Deng X, Bashlovkina V, Han F, Baumgartner S, Bendersky M Llms to the moon? reddit market sentiment analysis with large language models"},{"key":"2711_CR35","unstructured":"Perez E, Kiela D, Cho K (2021) True few-shot learning with language models. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 11054\u201311070. Curran Associates, Inc., ???. https:\/\/openreview.net\/forum?id=ShnM-rRh4T"},{"key":"2711_CR36","doi-asserted-by":"publisher","unstructured":"Lu Y, Bartolo M, Moore A, Riedel S, Stenetorp P (2022) Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8086\u20138098. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.556","DOI":"10.18653\/v1\/2022.acl-long.556"},{"key":"2711_CR37","unstructured":"Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, Chi EH, Le QV, Zhou D (2022) Chain-of-thought prompting elicits reasoning in large language models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. NIPS \u201922. Curran Associates Inc., Red Hook, NY, USA"},{"key":"2711_CR38","doi-asserted-by":"publisher","unstructured":"Wang B, Min S, Deng X, Shen J, Wu Y, Zettlemoyer L, Sun H (2023) Towards understanding chain-of-thought prompting: An empirical study of what matters. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2717\u20132739. Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.153","DOI":"10.18653\/v1\/2023.acl-long.153"},{"key":"2711_CR39","doi-asserted-by":"publisher","unstructured":"Xie T, Li Q, Zhang Y, Liu Z, Wang H (2024) Self-improving for zero-shot named entity recognition with large language models. In: Duh, K., Gomez, H., Bethard, S. (eds.) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pp. 583\u2013593. Association for Computational Linguistics, Mexico City, Mexico . https:\/\/doi.org\/10.18653\/v1\/2024.naacl-short.49","DOI":"10.18653\/v1\/2024.naacl-short.49"},{"key":"2711_CR40","doi-asserted-by":"publisher","unstructured":"Wang Q, Wang Z, Su Y, Tong H, Song Y (2024) Rethinking the bounds of LLM reasoning: Are multi-agent discussions the key? In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6106\u20136131. Association for Computational Linguistics, Bangkok, Thailand https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.331","DOI":"10.18653\/v1\/2024.acl-long.331"},{"key":"2711_CR41","doi-asserted-by":"publisher","unstructured":"Zhang J, Xu X, Zhang N, Liu R, Hooi B, Deng S (2024) Exploring collaboration mechanisms for LLM agents: A social psychology view. In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14544\u201314607. Association for Computational Linguistics, Bangkok, Thailand. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.782","DOI":"10.18653\/v1\/2024.acl-long.782"},{"key":"2711_CR42","unstructured":"AI@Meta: Llama 3.1 Model Card (2024). https:\/\/github.com\/meta-llama\/llama-models\/blob\/main\/models\/llama3_1\/MODEL_CARD.md"},{"key":"2711_CR43","unstructured":"Jiang AQ, Sablayrolles A, Mensch A, Bamford C, Chaplot DS, Casas D, Bressand F, Lengyel G, Lample G, Saulnier L, Lavaud LR, Lachaux M-A, Stock P, Scao TL, Lavril T, Wang T, Lacroix T, Sayed WE (2023) Mistral 7B https:\/\/arxiv.org\/abs\/2310.06825"},{"key":"2711_CR44","unstructured":"Team G (2024) Gemma 2: Improving Open Language Models at a Practical. https:\/\/arxiv.org\/abs\/2403.05530"},{"key":"2711_CR45","unstructured":"Team Q (2024) Qwen2.5: A Party of Foundation Models. https:\/\/qwenlm.github.io\/blog\/qwen2.5\/"},{"key":"2711_CR46","unstructured":"Tunstall L, Beeching E, Lambert N, Rajani N, Rasul K, Belkada Y, Huang S, Werra L, Fourrier C, Habib N, Sarrazin N, Sanseviero O, Rush AM, Wolf T (2023) Zephyr: Direct Distillation of LM Alignment https:\/\/arxiv.org\/abs\/2310.16944"},{"key":"2711_CR47","unstructured":"Microsoft: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone (2024). https:\/\/arxiv.org\/abs\/2404.14219"},{"key":"2711_CR48","unstructured":"Team G (2024) Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. https:\/\/arxiv.org\/abs\/2403.05530"},{"key":"2711_CR49","unstructured":"Anthropic: Claude 3.5 sonnet model card addendum. Technical report, Anthropic (September 2024). Technical Report. https:\/\/www.anthropic.com\/modelcards\/claude-35-sonnet-addendum"},{"key":"2711_CR50","unstructured":"OpenAI: GPT-4o Model Card (2024). https:\/\/openai.com\/index\/hello-gpt-4o\/"},{"key":"2711_CR51","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 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":"2711_CR52","doi-asserted-by":"publisher","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, Grave E, Ott M, Zettlemoyer L, Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440\u20138451. Association for Computational Linguistics, Online . https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.747","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"2711_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106315","volume":"175","author":"B Wang","year":"2024","unstructured":"Wang B, Li X, Yang Z, Guan Y, Li J, Wang S (2024) Unsupervised sentence representation learning with frequency-induced adversarial tuning and incomplete sentence filtering. Neural Netw 175:106315. https:\/\/doi.org\/10.1016\/j.neunet.2024.106315","journal-title":"Neural Netw"},{"key":"2711_CR54","doi-asserted-by":"publisher","unstructured":"Bai G, Liu J, Bu X, He Y, Liu J, Zhou Z, Lin Z, Su W, Ge T, Zheng B, Ouyang W (2024) MT-bench-101: A fine-grained benchmark for evaluating large language models in multi-turn dialogues. In: Ku, L.-W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7421\u20137454. Association for Computational Linguistics, Bangkok, Thailand. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.401","DOI":"10.18653\/v1\/2024.acl-long.401"},{"key":"2711_CR55","unstructured":"Hu EJ, shen Wallis P, Allen-Zhu Z, Li Y, Wang S, Wang L, Chen W (2022) LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=nZeVKeeFYf9"},{"key":"2711_CR56","doi-asserted-by":"publisher","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3982\u20133992. Association for Computational Linguistics, Hong Kong, China. https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02711-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02711-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02711-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T16:59:22Z","timestamp":1760547562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02711-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":56,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2711"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02711-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"26 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011370, the National Natural Science Foundation of China (32371114), the Characteristic Innovation Projects of Guangdong Colleges and Universities (No. 2018KTSCX049), the Guangdong Provincial Key Laboratory [No.2023B1212060076].","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The authors declare no Conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}