History, development, and principles of large language models: an introductory survey
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LLMs acquire a broad representation of linguistic structures and relationships through pre-training that allows them to perform a wide range of NLP tasks with little or no task-specific training data.
“Through their pre-training, LLMs acquire a broad representation of linguistic structures and relationships that allow them to perform a wide range of NLP tasks with little or no task-specific training data.3”
From Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions (2026) · cites this paper as [3]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1× · see citing works
“These aspects disproportionately affect already disadvantaged and marginalized groups [15, 22, 162].”
Pinker, S. A. (1994) · PsycEXTRA Dataset · doi:10.1037/e412952005-009 · Cited by 5,879 · see citing works
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637 · see citing works
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
SRILM - an extensible language modeling toolkit
Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 1× · see citing works
“This survey distinguishes itself from other reviews on PLMs or LLMs [4, 12–14], which often concentrate on specific aspects using technical language; this survey aims to elucidate LLMs and their principles in a more accessible manner, intending to maximize its full potential.”
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 1× · see citing works
“This survey distinguishes itself from other reviews on PLMs or LLMs [4, 12–14], which often concentrate on specific aspects using technical language; this survey aims to elucidate LLMs and their principles in a more accessible manner, intending to maximize its full potential.”
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004 · see citing works
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures
Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
Statistical methods for speech recognition
Jelinek, F. (1997) · Journal of the American Statistical Association · doi:10.2307/2670189 · Cited by 2,376 · see citing works
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 1,826 · see citing works
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · cited 1× · see citing works
“Initially, considering a text of length T , the most basic approach of positional encoding is counting.”
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,342 · see citing works
Emergent Abilities of Large Language Models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · see citing works
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works
Training Compute-Optimal Large Language Models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932 · see citing works
Training Compute-Optimal Large Language Models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.15556 · Cited by 929 · see citing works
Two decades of statistical language modeling: where do we go from here?
Rosenfeld, R. (2000) · Proceedings of the IEEE · doi:10.1109/5.880083 · Cited by 742 · see citing works
Extracting Training Data from Large Language Models
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works
Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P. (2021) · Applied Sciences · doi:10.3390/app11146421 · Cited by 718 · see citing works
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10291-4 · Cited by 685 · see citing works
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works
Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 665 · see citing works
Unified Pre-training for Program Understanding and Generation
Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642 · see citing works
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612 · see citing works
Talking about Large Language Models
Shanahan, M. (2024) · Communications of the ACM · doi:10.1145/3624724 · Cited by 381 · cited 1× · see citing works
“This survey distinguishes itself from other reviews on PLMs or LLMs [4, 12–14], which often concentrate on specific aspects using technical language; this survey aims to elucidate LLMs and their principles in a more accessible manner, intending to maximize its full potential.”
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.891 · Cited by 361 · see citing works
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Li, Y., Wang, S., Ding, H., & Chen, H. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626869 · Cited by 334 · see citing works
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.98 · Cited by 306 · see citing works
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
Zheng, Q., Xia, X., Zou, X., Dong, Y., Wang, S., Xue, Y., Shen, L., Wang, Z., Wang, A., Li, Y., et al. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599790 · Cited by 263 · see citing works
Hate speech detection and racial bias mitigation in social media based on BERT model
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · PLOS ONE · doi:10.1371/journal.pone.0237861 · Cited by 260 · see citing works
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Automating code review activities by large-scale pre-training
Li, Z., Lu, S., Guo, D., Duan, N., Jannu, S., Jenks, G., Majumder, D., Green, J., Svyatkovskiy, A., Fu, S., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549081 · Cited by 226 · see citing works
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Mashrur, A., Luo, W., Zaidi, N. A., & Robles‐Kelly, A. (2020) · IEEE Access · doi:10.1109/access.2020.3036322 · Cited by 224 · see citing works
Recurrent neural network based language modeling in meeting recognition
Kombrink, S., Mikolov, T., Karafiát, M., & Burget, L. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-720 · Cited by 207 · see citing works
An omics perspective on drug target discovery platforms
Paananen, J., & Fortino, V. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz122 · Cited by 207 · see citing works
Chatbots in customer service: Their relevance and impact on service quality
Misischia, C. V., Poecze, F., & Strauß, C. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.03.055 · Cited by 185 · see citing works
Bommarito, M. J., & Katz, D. M. (2022) · doi:10.2139/ssrn.4314839 · Cited by 181 · see citing works
Comprehensive review of text-mining applications in finance
Gupta, A., Dengre, V., Kheruwala, H. A., & Shah, M. (2020) · Financial Innovation · doi:10.1186/s40854-020-00205-1 · Cited by 164 · see citing works
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Arora, A., & Arora, A. (2023) · The Lancet · doi:10.1016/s0140-6736(23)00216-7 · Cited by 153 · see citing works
Med-HALT: Medical Domain Hallucination Test for Large Language Models
Pal, A., Umapathi, L. K., & Sankarasubbu, M. (2023) · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/2023.conll-1.21 · Cited by 152 · see citing works
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Steenhoek, B. J., Rahman, M. M., Jiles, R., & Wei, L. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00188 · Cited by 141 · see citing works
Xiao, C., Xu, S. X., Zhang, K., Wang, Y., & Xia, L. (2023) · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) · doi:10.18653/v1/2023.bea-1.52 · Cited by 138 · see citing works
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Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.330 · Cited by 131 · see citing works
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Meade, N., Poole-Dayan, E., & Reddy, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.132 · Cited by 116 · see citing works
Kim, B., Kim, H., Lee, S., Lee, G., Kwak, D., Hyeon, J. D., Park, S., Kim, S., Kim, S., Seo, D., et al. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.274 · Cited by 97 · see citing works
On protecting the data privacy of Large Language Models (LLMs) and LLM agents: A literature review
Yan, B., Li, K., Xu, M., Dong, Y., Zhang, Y., Ren, Z., & Cheng, X. (2025) · High-Confidence Computing · doi:10.1016/j.hcc.2025.100300 · Cited by 91 · see citing works
Drug discovery companies are customizing ChatGPT: here’s how
Savage, N. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01788-7 · Cited by 86 · see citing works
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Haley, B., & Roudnicky, F. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.006 · Cited by 73 · see citing works
Decoding the Threat Landscape : ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks
Falade, P. V. (2023) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2390533 · Cited by 73 · see citing works
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Chu, Z., Wang, Z., & Zhang, W. (2024) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3682112.3682117 · Cited by 72 · see citing works
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.06745 · Cited by 67 · see citing works
Mei, K., Fereidooni, S., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594109 · Cited by 56 · see citing works
Gorgulla, C., Jayaraj, A., Fackeldey, K., & Arthanari, H. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102156 · Cited by 49 · see citing works
He, J., Xu, B., Yang, Z., Han, D., Yang, C., & Lo, D. (2022) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3524610.3527897 · Cited by 43 · see citing works
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Tay, Y., Wei, J., Chung, H. W., Tran, V., So, D. R., Shakeri, S., Garcia, X., Zheng, S., Rao, J., Chowdhery, A., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.91 · Cited by 41 · see citing works
Sonkar, S., Liu, N., Mallick, D., & Baraniuk, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.130 · Cited by 35 · see citing works
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Iu, K. Y., & Wong, V. M.-Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4339839 · Cited by 34 · see citing works
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Li, Z., Wang, C., Wang, S., & Gao, C. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623120 · Cited by 32 · see citing works
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He, J., Zhou, X., Xu, B., Zhang, T., Kim, K., Yang, Z., Thung, F., Irsan, I. C., & Lo, D. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3635711 · Cited by 32 · see citing works
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Zhang, Z., Zohren, S., & Roberts, S. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3613600 · Cited by 29 · see citing works
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Chen, Y., Ding, N., Zheng, H.-T., Liu, Z., Sun, M., & Zhou, B. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679665 · Cited by 26 · see citing works
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Zentner, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4314180 · Cited by 25 · see citing works
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Leboukh, F., Aduku, E. B., & Ali, O. (2023) · Journal of Politics and Ethics in New Technologies and AI · doi:10.12681/jpentai.35166 · Cited by 20 · see citing works
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Wang, Z., Narasimhan, G., Yao, X., & Zhang, W. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00073 · Cited by 20 · see citing works
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Zhang, W. (2024) · AI Magazine · doi:10.1002/aaai.12189 · Cited by 19 · see citing works
Advancing Graph Counterfactual Fairness Through Fair Representation Learning
Wang, Z., Chu, Z., Blanco, R., Chen, Z., Chen, S., & Zhang, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70368-3_3 · Cited by 16 · see citing works
Answer Summarization for Technical Queries: Benchmark and New Approach
Yang, C., Xu, B., Thung, F., Shi, Y., Zhang, T., Yang, Z., Zhou, X., Shi, J., He, J., Han, D., et al. (2022) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3551349.3560421 · Cited by 14 · see citing works
Tu, S., Zhang, Z., Yu, J., Li, C., Zhang, S., Yao, Z., Hou, L., & Li, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615484 · Cited by 14 · see citing works
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Jin, H., Wei, W., Wang, X., Zhang, W., & Wu, Y. (2023) · IEEE International Conference on Cognitive Machine Intelligence (CogMI) · doi:10.1109/cogmi58952.2023.00025 · Cited by 14 · see citing works
Improving Fairness in Machine Learning Software via Counterfactual Fairness Thinking
Yin, Z., Wang, Z., & Zhang, W. (2024) · IEEE/ACM International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3643531 · Cited by 13 · see citing works
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Saxena, N. A., Zhang, W., & Shahabi, C. (2023) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611977653.ch110 · Cited by 12 · see citing works
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Pagliaro, C. A., Mehta, D. B., Shiao, H.-T., Wang, S., & Xiong, L. (2021) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3490354.3494388 · Cited by 9 · see citing works
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Chinta, S. V., Fernandes, K., Cheng, N., Fernandez, J., Yazdani, S., Yin, Z., Wang, Z., Wang, X., Xu, W., Liu, J., et al. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00199 · Cited by 8 · see citing works
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Doan, T. V., Wang, Z., Hoang, N., & Zhang, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679090 · Cited by 6 · see citing works
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Workshop, B., :, Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., et al. (2022) · arXiv · doi:10.48550/arxiv.2211.05100
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A comprehensive survey on integrating large language models with knowledge-based methods
Yang, W., Some, L., Bain, M., & Kang, B. H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113503 · Cited by 88 · see citing works
Fairness in Large Language Models: A Taxonomic Survey
Chu, Z., Wang, Z., & Zhang, W. (2024) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3682112.3682117 · Cited by 72 · see citing works
Generative AI in depth: A survey of recent advances, model variants, and real-world applications
Yazdani, S., Singh, A., Saxena, N., Wang, Z., Palikhe, A., Pan, D., Pal, U., Yang, J., & Zhang, W. (2025) · Journal of Big Data · doi:10.1186/s40537-025-01247-x · Cited by 40 · see citing works
FG-SMOTE: Towards Fair Node Classification with Graph Neural Network
Wang, Z., Yin, Z., Zhang, Y., Yang, L., Zhang, T., Pissinou, N., Cai, Y., Hu, S., Li, Y., Zhao, L., et al. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715082 · Cited by 22 · see citing works
AI-Enabled Microfluidics for Respiratory Pathogen Detection
Zhang, D., Lv, X., Jiang, H., Fan, Y., Liu, K., Wang, H., & Deng, Y. (2025) · Sensors · doi:10.3390/s25185791 · Cited by 19 · see citing works
Deep learning in template-free de novo biosynthetic pathway design of natural products
Xie, X., Gui, L., Qiao, B., Wang, G., Huang, S., Zhao, Y., & Sun, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae495 · Cited by 19 · see citing works
Nayinzira, J. P., & Adda, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.11.118 · Cited by 15 · see citing works
A Review of Prompt Engineering Techniques for Large Language Models
Naser, M. Z. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2607553 · Cited by 17 · see citing works
How LLMs Are Shaping the Future of Virtual Reality
Özkaya, S., Berrezueta-Guzmán, J., & Wagner, S. (2025) · IEEE Access · doi:10.1109/access.2025.3631594 · Cited by 14 · see citing works
Guerino, G. C., Rodrigues, L., Capeleti, B. S., Mello, R. F., Freire, A. P., & Zaina, L. A. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05005-2_20 · Cited by 11 · see citing works
Swamped with Too Many Articles? GraphRAG Makes Getting Started Easy
Ngangmeni, J., & Rawat, D. B. (2025) · AI · doi:10.3390/ai6030047 · Cited by 10 · see citing works
How to Write Effective Prompts for Screening Biomedical Literature Using Large Language Models
Colangelo, M. T., Guizzardi, S., Meleti, M., Calciolari, E., & Galli, C. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5010015 · Cited by 10 · see citing works
Human vs. Machine: Comparing AI-Generated and Human-Written Psychological Reports
Lockwood, A. B., Farmer, R. L., Shergill, G., Benson, N. F., & Gilbert, K. (2025) · Journal of Psychoeducational Assessment · doi:10.1177/07342829251346623 · Cited by 12 · see citing works
Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy
Alizadeh, N., Belchev, B., Saurabh, N., Kelbert, P., & Castor, F. (2025) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr66628.2025.00109 · Cited by 10 · see citing works
Artificial intelligence‐enabled digital biomedical engineering
Song, P., Tang, X., Lv, X., Reis, R. L., Chen, X., Bai, L., & Su, J. (2025) · BMEMat · doi:10.1002/bmm2.70049 · Cited by 9 · see citing works
Test case generation using large language models: a systematic literature review
Tasarsu, M., Tokmak, A. V., & Catal, C. (2026) · Cluster Computing · doi:10.1007/s10586-026-06021-z · Cited by 10 · see citing works
Who wrote this? Evaluating the reliability of AI detection tools in higher education
Van Vlasselaer, M., Van Droogenbroeck, F., & Spruyt, B. (2026) · International Journal for Educational Integrity · doi:10.1007/s40979-026-00226-w · Cited by 14 · see citing works
Graph Fairness via Authentic Counterfactuals: Tackling Structural and Causal Challenges
Wang, Z., Yin, Z., Liu, F., Liu, Z., Lisetti, C., Yu, R., Wang, S., Liu, J., Ganapati, S., Zhou, S., et al. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715081 · Cited by 7 · see citing works
Extracting airline emission KPIs from sustainability reports using large language models (LLMs)
Martín-Domingo, L., Fernandez, J. B., Efthymiou, M., & Ali, M. I. (2025) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2025.101599 · Cited by 6 · see citing works
Smutný, Z., & Sudzina, F. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2573037 · Cited by 7 · see citing works
How is language intelligence evolving? A multi-dimensional survey of large language models
Chen, X., Zhou, L., Chen, J., Wang, G., & Li, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130637 · Cited by 7 · see citing works
Next-Gen orientation: supporting international students with generative AI NPCs in VR
Berrezueta-Guzman, S., & Wagner, S. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1799323 · Cited by 6 · see citing works
Fairness in Large Language Models in Three Hours
Doan, T. V., Wang, Z., Hoang, N., & Zhang, W. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679090 · Cited by 6 · see citing works
Awan, S. A., Khan, M. A., Khan, A. A., Sathio, A. A., Alsayaydeh, J. A. J., Bacarra, R., Herawan, S. G., & Aziz, R. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01330-3 · Cited by 5 · see citing works
Exploring large language models’ responses to moral reasoning dilemmas
Nabizadeh, D., Walker, D., Han, H., & Laird, E. (2025) · Ethics & Behavior · doi:10.1080/10508422.2025.2601022 · Cited by 5 · see citing works
Zouakia, Z., Logak, E., Szymczak, A., Jaïs, J., Burgun, A., & Tsopra, R. (2026) · JMIR Medical Education · doi:10.2196/82116 · Cited by 10 · see citing works
Evaluating chatbots in psychiatry: Rasch-based insights into clinical knowledge and reasoning
Chang, Y., Huang, S., Hsu, W., & Liu, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330303 · Cited by 5 · see citing works
Evaluating the Potential of ChatGPT to Support Climate Risk and Adaptation Assessment
Wilby, R. L. (2025) · Climate Resilience and Sustainability · doi:10.1002/cli2.70013 · Cited by 4 · see citing works
An Agent-Based RAG Architecture for Intelligent Tourism Assistance: The Valencia Case Study
Bonetti, A., Salcedo-Puche, A., Vila‐Francés, J., García, X. B., Fernández-Vargas, E., Magdalena‐Benedito, R., & Soria‐Olivas, E. (2025) · Tourism and Hospitality · doi:10.3390/tourhosp6050266 · Cited by 5 · see citing works
GAI Versus Teacher Scoring: Which is Better for Assessing Student Performance?
Li, X. R., Zappatore, M. S., Li, T., Zhang, W., Tao, S., Wei, X., Zhou, X., Guan, N., & Chan, A. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3572518 · Cited by 5 · see citing works
Large Language Models (LLMs): A systematic study in Administration and Business
Pessanha, G. R. G., Vieira, A. G., & Brandão, W. C. (2024) · RAM. Revista de Administração Mackenzie · doi:10.1590/1678-6971/eramd240059 · Cited by 5 · see citing works
Genç, O. (2025) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2025.2574556 · Cited by 3 · see citing works
A Survey of Hallucination in Large Language Models
何, 金. (2026) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2026.151016 · Cited by 3 · see citing works
Matheus, J., Tiwari, S., Szemat, W., & Amaya, D. (2025) · Offshore Technology Conference · doi:10.4043/35742-ms · Cited by 3 · see citing works
Marco, G. D., Niederwieser, E., & Siegele, D. (2025) · Buildings · doi:10.3390/buildings15183352 · Cited by 3 · see citing works
Advancing industrial sustainability research: a domain-specific large language model perspective
Huang, Y. (2025) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-025-03179-9 · Cited by 3 · see citing works
Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions
Lee, R. Y., Li, K. S., Sibley, J., Cohen, T., Lober, W. B., Dotolo, D. G., & Kross, E. K. (2026) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2025.09.025 · Cited by 5 · see citing works
Hörtling, S., Bause, K., & Albers, A. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-0289 · Cited by 2 · see citing works
Den samtida receptionen och värderingen av AI-genererad litteratur i sverige
Helsing, D., Forslid, T., & Ohlsson, A. (2025) · Tidskrift för litteraturvetenskap · doi:10.54797/tfl.v55i1.55919 · Cited by 2 · see citing works
Generative AI in Game Development: A Bibliometric Study with an Auditable Computing Workflow
Xia, S. (2025) · International Conference on Artificial Intelligence, Virtual Reality and Interaction Design · doi:10.1145/3777730.3777843 · Cited by 3 · see citing works
Exploring More-than-human Ethics of Large Language Models in Social Service Delivery
Seniutis, M., Sas, A., & Gružauskas, V. (2026) · Ethics and Social Welfare · doi:10.1080/17496535.2025.2593823 · Cited by 2 · see citing works
Administrative Decision-Making with Generative AI: The Challenge of Epistemic Boundedness
Kim, Y., Kim, J., Kim, T., & Cho, H.-C. (2026) · Administration & Society · doi:10.1177/00953997251409156 · Cited by 2 · see citing works
Rjoub, G., Bentahar, J., Almobydeen, S. B., Pedrycz, W., & Irjoob, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133192 · Cited by 2 · see citing works
Generative AI as a Knowledge Distribution System
Lavazza, A., & Farina, M. (2026) · Social Epistemology · doi:10.1080/02691728.2026.2623557 · Cited by 3 · see citing works
Utilizing LLMs and ontologies to query educational knowledge graphs
Antοniou, C., & Bassiliades, N. (2024) · Pan-Hellenic Conference on Progress in Computing and Informatics (PCI) · doi:10.1145/3716554.3716598 · Cited by 2 · see citing works
Picazo-Sanchez, P., & Ortiz-Martin, L. (2026) · Data · doi:10.3390/data11050122 · Cited by 1 · see citing works
“In the 1990s, the existing NLP models were based on neural networks [15].”
Evaluating Large Language Models: Challenges, Limitations, and Future Directions
Li, L.-H., & Nugroho, A. C. (2025) · Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) · doi:10.1109/ectidamtncon64748.2025.10961969 · Cited by 1 · see citing works
Sattorova, Z. I., Hossain, R. R., Suryasa, I. W., Ugli, Y. U. V., & Rahman, F. (2025) · International Conference on Computational Innovations and Engineering Sustainability (ICCIES) · doi:10.1109/iccies63851.2025.11032453 · Cited by 1 · see citing works
Tan, B., Ren, J., Li, Y., Ding, S., & Chaddad, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9884-4_22 · Cited by 1 · see citing works
Recent Advances and Future Directions in Literature-Based Discovery
Kastrin, A., Cestnik, B., & Nada Lavrač, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03028-3_12 · Cited by 3 · see citing works
Patil, S. S., Ramteke, M., Verma, M., Chandra, T., & Rathore, A. S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11600-z · Cited by 1 · see citing works
From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design
Gupta, S., Rezayat, S., Burmester, G., Durak, U., Ma, H., & Hartmann, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08620-4_9 · Cited by 1 · see citing works
An Overview of Large Language Models in 6G Radio Access Networks
Çimen, S., Altıntaş, M., Duru, İ., Karahan, S. N., & Yazıcı, İ. (2025) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce67514.2025.11257883 · Cited by 1 · see citing works
Retrieval-Augmented Large Language Models for Computer Architecture Learning and Design Assistance
de Souza, W. J., Neto, H. T. M., & de Freitas, H. C. (2025) · International Journal of Computer Architecture Education. · doi:10.5753/ijcae.2025.6540 · Cited by 1 · see citing works
Yadav, I., Schindler, S., Peters, D., & Klinger, R. (2026) · Semantic Web · doi:10.1177/22104968261453132 · Cited by 1 · see citing works
Tool or Companion? Reframing Conversational AI to Prevent Psychological Harm
Novelli, A. M., Shergill, S. S., & Teixeira, A. S. (2026) · JMIR Mental Health · doi:10.2196/99354 · Cited by 1 · see citing works
Birdir, S. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8769-6.ch008 · Cited by 1 · see citing works
AI and Challenges to Personal Data and Data Protection in Research
Lindblom, L. (2025) · CTE · doi:10.3384/9789181181814
Exploring Large Language Models' Responses to Moral Reasoning DilemmasPreprint
Nabizadeh, D., Walker, D. I., Han, H., & Laird, E. (2025) · Research Square · doi:10.21203/rs.3.rs-6823916/v1
Lee, E., Do, H.-D., Kim, M., & Oh, D. (2025) · Applied Sciences · doi:10.3390/app15137561
Ren, Y., Cui, Y., & Su, J. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11064723
MASON - A Multi-Agent LLM Framework for No-Code Development
Palayamkot, M. R., & Karim, K. (2025) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v6i1.36068
Metzner, O., Wang, Y., de Melo, G., Symes, W., Huang, Y., & Lazarides, R. (2025) · British Journal of Educational Psychology · doi:10.1111/bjep.70013
Coriell, J., Kiremire, A. R., Cruse, K., & Long, W. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--55907
Toda, K., Nishi, T., & Liu, Z. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-03534-9_20
Rjoub, G., Bentahar, J., Almobydeen, S. B., Pedrycz, W., & Irjoob, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5668177
CABENCH: Benchmarking Composable Ai for Solving Complex Tasks through Composing Ready-to-Use Models
Pham, T.-T., Luong, D.-Q., Duong, M.-Q., Nguyen, T.-H., Nguyen, T.-T., Nguyen, S., & Vo, H. D. (2025) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse68178.2025.11309631
RAG-Enhanced LLM for Evaluating Grid Energy Storage Hosting Capacity
Zhang, P., Zhou, H., Tao, Y., Wang, Q., & Lu, M. (2025) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic68075.2025.11324841
Strategic Steering of Large Language Models via Game-Theoretic Action Space Optimization
Lavebrink, S., Brynielsson, J., Cohen, M., Kamrani, F., Limér, C., Lindström, M., & Vangeli, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14107-1_41
Dikovitsky, V. V. (2025) · Transaction Kola Science Centre · doi:10.37614/2949-1215.2025.16.3.005
Differential Fuzzing Go Compilers using LLMs: A Methodological Proposal
Terres, L. P. G., & Feitosa, S. D. S. (2025) · Escola Regional de Engenharia de Software (ERES) · doi:10.5753/eres.2025.16857
Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing SarcomaPreprint
Huang, J., Batool, A., Gu, Z., Zhao, Z., Yao, B., Black, J. O., Davis, J. L., Al‐Ibraheemi, A., DuBois, S., Barkauskas, D. A., et al. (2026) · SSRN Electronic Journal · doi:10.64898/2026.02.20.707103
Applying LLMs for Automated Extraction of Business Logic from Source Code into DMN ModelsPreprint
van de Hoef, A., Vranken, H., Leewis, S., & Smit, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6299556
Mini-review: considering impacts of artificial intelligence on the development of measurement scales
Stanton, J. M. (2026) · Frontiers in Organizational Psychology · doi:10.3389/forgp.2026.1787155
Wang, Q., Li, J., Li, X., & Deng, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-40043-2
Unsupervised Learning for Discovering Language Patterns in Historical Educational Texts
Jumaniyazov, M. D., Nadejda, T., Temirov, A., Tursunov, X. I., Turgunova, D., & Avliyaqulov, T. (2026) · International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) · doi:10.1109/iciscois62701.2026.11448016
Clinical Informatics and Artificial Intelligence in Healthcare
Greenwood, J. D., & Qian, R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-2577-4.ch004
Sharafi, Y., Teshnehlab, M., & Sadighi, N. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115210
Chen, M. W., Yan, Y., Shen, X., Gao, H., & Chen, Z. (2026) · BioData Mining · doi:10.1186/s13040-026-00547-z
The Gentler Researcher: Examining Research Bias and Gender Stereotypes in Sikt AI Chat
Skaar, Ø. O., & Skaar, C. M. (2026) · Nordic Journal of Digital Literacy · doi:10.18261/njdl.21.1.3
Evaluating LLM-Based Process Explanations Under Progressive Behavioral-Input Reduction
van Oerle, P., Bemthuis, R., & Bukhsh, F. A. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-16234-2_10
Lussier, P., & Décary-Hêtu, D. (2025) · Canadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · doi:10.3138/cjccj-2026-0201
Ebara, M., Kawazoe, Y., Seki, T., Shinohara, E., Nakazawa, E., & Ohe, K. (2026) · AI and Ethics · doi:10.1007/s43681-026-00998-4
LLMs with Tools: Empirical Evaluation of the Thought-Action-Observation Paradigm
Usman, M., Iltaf, N., Shakoori, M. R., & Rana, T. A. (2026) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi68585.2026.11539909
Next-Generation AI: A Review of Large Language Models, Pipelines, and Security Challenges
Gupta, A., & Kumari, D. R. (2025) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3793449.3793505
Lupin: Spatial Resource Stealing with Outlier-First Encoding for Mixed-Precision LLM Acceleration
Kim, T., Han, S., Kim, S., Byeon, G., Lee, J. H., & Hong, S. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539287
Using Large Language Models to Generate New Features from Text Data for Loss Prediction
Gan, G., & Shultz, C. (2026) · Variance · doi:10.66573/001c.162423
Dewi, A. N., Sørensen, B., & Thellefsen, M. (2026) · Journal of Documentation · doi:10.1108/jd-04-2026-0185
Generative AI, Architecture, and Mathematics: Supporting Sustainable Development Goals
Henriques, D. P., & Ostwald, M. J. (2026) · Springer eBooks · doi:10.1007/978-3-032-02376-6_20-1
Artificial Intelligence and the Public Library: Uses, Challenges, and Needs
Cho, H., Bossaller, J., Nicolosi, G., Parishani, Z., & Adkins, D. (2026) · Internet Reference Services Quarterly · doi:10.1080/10875301.2026.2694759
GÜZEL, M. E. (2026) · Uluslararası Dil Edebiyat ve Kültür Araştırmaları Dergisi · doi:10.37999/udekad.1912495
Polkovnikova, N. A. (2026) · Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S O Makarova · doi:10.21821/2309-5180-2026-18-3-538-557
A Linux Test Question Generation Method for Knowledge-Enhanced Large Language Models
Yang, J., & Wang, J. (2026) · International Conference on Computer Technology and Sustainable Development · doi:10.1145/3814210.3814216
Model Training with PEFT: Low-Rank Adaptation (LoRA) and Alternative Strategies
Destan, Z., & Alp, E. (2026) · International Symposium on AI-Driven Engineering Systems (ISADES) · doi:10.1109/isades69945.2026.11608113
Overview of RAG-based and LLM-based approaches to personalization in healthcare AI applications
Althobaiti, M., & Jun, M. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1785766
Lin, Z., Li, H., & Du, J. (2026) · The Electronic Library · doi:10.1108/el-11-2025-0438
Transformers as the Foundation of Large Language Models: A Comprehensive Review
Shaikh, M. R., Shahryar, N., Mahboob, K., Naiyar, Q. U. A., & Talha, M. (2025) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1632
Moralles, C., Costa, L. A. L. F. D., Silva, E. P., Prado, G. L. E., Schardosim, T. D. C., Rigo, S. J., Kunst, R., Souza, V. C., & Roehrs, A. (2026) · Anais do XVIII Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP 2026) · doi:10.5753/sbcup.2026.20678
Fahy, C., McDonald, N., Keenan, K., & Corrigan, S. (2026) · AI Magazine · doi:10.1002/aaai.70073
- Authors
- Wang, Z., Chu, Z., Doan, T. V., Ni, S., Yang, M., & Zhang, W.
- Affiliations
- Florida International University (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Zhang, Wenbin
- Venue
- AI and Ethics
- Published
- 2024-10-14
- Volume
- 5
- Issue
- 3
- Pages
- 1955-1971
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W4403401276
- Cited by
- 109
- References
- 110
- Citations by year
- 2026: 47 · 2025: 56 · 2024: 6
- FWCI
- 16.15
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- AI and Ethics (publishedVersion)
- arXiv (Cornell University) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2245895)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- large language models, language models, statistical language models, natural language generation, text prediction, LLM limitations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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