Knowledge management in organization and the large language models
DOI:
https://doi.org/10.21638/spbu18.2024.309Abstract
Purpose: to summarize, classify and analyze current academic papers on the use of large language models (LLM) in knowledge management in organization.
Methodology: systematic literature review was conducted. It was based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. 75 papers were selected for the analysis, including academic papers and reports of consulting companies published since 2020.
Findings: four main research areas have been identified: (1) LLM implementation issues; (2) the impact of LLM on knowledge management efficiency; the application of LLM in the processes of (3) knowledge usage and (4) knowledge creation. Within each area, the key papers and open questions have been reviewed.
Originality and contribution: the paper presents a systematic review of current publications, proposes a classification of research topics, and identifies potential directions for new research. The study also considers limitations hindering the implementation of LLM in the organization's knowledge management practice.
Keywords:
generative AI, large language models, knowledge management, systematic literature review
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Литература на русском языке
Долотов А. 2024. Сложности интеграции: что поможет ускорить GPT-трансформацию бизнеса. Forbes. [Электронный ресурс]. https://www.forbes.ru/mneniya/507501-sloznosti-integracii-cto-pomozet-uskorit-gpt-transformaciu-biznesa (дата обращения: 05.04.2024).
Яков и Партнеры. 2023. Искусственный интеллект в России — 2023: тренды и перспективы. [Электронный ресурс]. https://yakovpartners.ru/publications/ai-future/ (дата обращения: 05.04.2024).
References in Latin Alphabet
Agarwal R., Singh A., Zhang L. M., Bohnet B., Rosias L. et al. 2024. Many-shot in-context learning. arXiv preprint. https://doi.org/10.48550/arXiv.2404.11018
Agostini, L., Nosella, A., Sarala, R., Spender, J. C., Wegner, D. (2020). Tracing the evolution of the literature on knowledge management in inter-organizational contexts: a bibliometric analysis. Journal of Knowledge Management 24 (2): 463–490. http://dx.doi.org/10.1108/JKM-07-2019-0382
Alavi M., Leidner D. 2001. Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly 25 (1): 107–136. https://doi.org/10.2307/3250961
Alavi M., Leidner D., Mousavi R. 2024. A knowledge management perspective of generative artificial intelligence. Journal of the Association for Information Systems 25 (1): 1–12. https://doi.org/10.17705/1jais.00859
Anderson G., Dunlap K., Rademacher J., McMenamy N. 2009 Metaplan: A mutual methodology for interdisciplinary research. SoTL Commons Conference: 104. [Electronic resource]. https://digitalcommons.georgiasouthern.edu/sotlcommons/SoTL/2009/104 (accessed: 28.06.2024).
Asudani D. S., Nagwani N. K., Singh P. 2023. Impact of word embedding models on text analytics in deep learning environment: A review. Artificial Intelligence Review 56 (9): 10345–10425. https://doi.org/10.1007/s10462-023-10419-1
Azeem, M., Ahmed, M., Haider, S., Sajjad, M. 2021. Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation. Technology in Society 66: 101635. https://doi.org/10.1016/j.techsoc.2021.101635
Babaei G., Giudici P. 2024. GPT classifications, with application to credit lending. Machine Learning with Applications 16: 100534. https://doi.org/10.1016/j.mlwa.2024.100534
Beheshti A., Yang J., Sheng Q., Benatallah B., Casati F., Dustdar Sh., Nezhad H. R., Zhang X., Xue Sh. 2023. ProcessGPT: Transforming business process management with generative artificial intelligence. In: 2023 IEEE International Conference on Web Services (ICWS); 731–739. https://doi.org/10.1109/ICWS60048.2023.00099
Berg J. M., Raj M., Seamans R. 2023. Capturing value from artificial intelligence. Academy of Management Discoveries 9 (4): 424–428. https://doi.org/10.5465/amd.2023.0106
Bilgram V., Laarmann F. 2023. Accelerating innovation with generative AI: AI-augmented digital prototyping and innovation methods. IEEE Engineering Management Review 51 (2): 18–25. https://doi.org/10.1109/EMR.2023.3272799
Brossar M., Corbo J., Klaeyle M., Wiseman B. 2022. Deep learning in product design. McKinsey & Company. [Electronic resource]. https://www.mckinsey.com/capabilities/operations/our-insights/deep-learning-in-product-design (accessed: 02.05.2024).
Brown T., Mann B., Ryder N., Subbiah M., Kaplan J. et al. 2020. Language models are few-shot learners. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). [Electronic resource]. https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf (accessed: 09.04.2024).
Carrera-Rivera A., Ochoa W., Larrinaga F., Lasa G. 2022. How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX 9: 101895. https://doi.org/10.1016/j.mex.2022.101895
Castelvecchi D. 2023. How will AI change mathematics? Rise of chatbots highlghts discussion. Nature 615: 15–16. [Electronic resource]. https://www.nature.com/articles/d41586-023-00487-2 (accessed: 28.06.2024).
Chang Y., Wang, X., Wang, J., Wu Y., Yang L. et al. 2024. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15 (3): 1–45. https://doi.org/10.1145/3641289
Chen B., Sætre R., Miyao Y. 2024. A comprehensive evaluation of inductive reasoning capabilities and problem solving in large language models. In: Findings of the Association for Computational Linguistics: EACL 2024; 323–339. [Electronic resource]. https://aclanthology.org/2024.findings-eacl.22 (accessed: 29.04.2024).
Chui M., Hazan E., Roberts R., Singla A., Smaje K. et al. 2023. The economic potential of generative AI. The next productivity frontier. McKinsey & Company. [Electronic resource]. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier#/ (accessed: 23.04.2024).
Dalkir K. 2023. Knowledge Management in Theory and Practice. 4th ed. The MIT Press.
Dell'Acqua F., McFowland E., Mollick E., Lifshitz-Assaf H., Kellogg K. et al. 2023. Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24–013. [Electronic resource]. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed: 22.04.2024).
Dumas M., Fournier F., Limonad L., Marrella A., Montali M. et al. 2023 AI-augmented business process management systems: A research manifesto. ACM Transactions on Management Information Systems 14 (1): 1–19. https://doi.org/10.1145/3576047
Durst S., Foli S., Edvardsson I. R. 2024. A systematic literature review on knowledge management in SMEs: Current trends and future directions. Management Review Quarterly 74: 263–288. https://doi.org/10.1007/s11301-022-00299-0
Eisfeldt A., Shubert G., Zhang M. 2023. Generative AI and firm value. NBER Working Paper No. 31222. [Electronic resource]. http://www.nber.org/papers/w31222 (accessed: 22.04.2024).
Eloundou T., Manning S., Mishkin P., Rock D. 2023. GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint. https://doi.org/10.48550/arXiv.2303.10130
Fahland D., Fournier F., Limonad L., Skarbovsky I., Swevels A. J. 2024. How well can large language models explain business processes? arXiv preprint. https://doi.org/10.48550/arXiv.2401.12846
Felten E. W., Raj M., Seamans R. 2023. Occupational heterogeneity in exposure to generative AI. SSRN: 4414065. https://dx.doi.org/10.2139/ssrn.4414065
Galera-Zarco C., Floros G. 2023. A deep learning approach to improve built asset operations and disaster management in critical events: An integrative simulation model for quicker decision making. Annals of Operations Research (Early Access). https://doi.org/10.1007/s10479-023-05247-z
Gao Y., Xiong Y., Gao X., Jia K., Pan J. et al. 2023. Retrieval-augmented generation for large language models: A survey. arXiv preprint. https://doi.org/10.48550/arXiv.2312.10997
Gaviria-Marin, M., Merigó, J.M., Baier-Fuentes, H. 2019. Knowledge management: a global examination based on bibliometric analysis. Technological Forecasting and Social Change 140: 194-220. https://doi.org/10.1016/j.techfore.2018.07.006
Grosse R., Bae J., Anil C., Elhage N., Tamkin A. et al. 2023. Studying large language model generalization with influence functions. arXiv preprint. https://doi.org/10.48550/arXiv.2308.03296
Guo T., Nan B., Liang Z., Guo Z., Chawla N. et al. 2023. What can large language models do in chemistry? A comprehensive benchmark on eight tasks. In: Advances in Neural Information Processing Systems (NeurIPS 2023); 59662–59688. [Electronic resource]. https://proceedings.neurips.cc/paper_files/paper/2023/hash/bbb330189ce02be00cf7346167028ab1-Abstract-Datasets_and_Benchmarks.html (accessed: 29.04.2024).
Han J., Sarica S., Shi F., Luo J. 2022. Semantic networks for engineering design: State of the art and future directions. Journal of Mechanical Design 144 (2): 020802. https://doi.org/10.1115/1.4052148
Han S. J., Ransom K. J., Perfors A., Kemp C. 2024. Inductive reasoning in humans and large language models. Cognitive Systems Research 83: 101155. https://doi.org/10.1016/j.cogsys.2023.101155
Harfouche A., Quinio B., Saba M., Saba P. 2023. The recursive theory of knowledge augmentation: Integrating human intuition and knowledge in artificial intelligence to augment organizational knowledge. Information Systems Frontiers 25: 55–70. https://doi.org/10.1007/s10796-022-10352-8
Harrer S. 2023. Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. eBioMedicine 90: 104512. https://doi.org/10.1016/j.ebiom.2023.104512
Hendrycks D., Burns C., Basart S., Zou A., Mazeika M. et al. 2021. Measuring massive multitask language understanding. In: Proceedings of the International Conference on Learning Representations. https://doi.org/10.48550/arXiv.2009.03300
Hogan A., Gutierrez C., Cochez M., de Melo G., Kirrane S. et al. 2022. Knowledge Graphs. Springer. https://doi.org/10.1007/978-3-031-01918-0
Horton J. 2023. Large language models as simulated economic agents: What can we learn from homo silicus? NBER Working Paper No. 31122. [Electronic resource]: http://www.nber.org/papers/w31122 (accessed: 18.04.2024).
Hu E., Shen Y., Wallis P., Allen-Zhu Z., Li Y. et al. 2021. LORA: Low-rank adaptation of large language models. arXiv preprint. https://doi.org/10.48550/arXiv.2106.09685
Hu X., Tian Y., Nagato K., Nakao M., Liu A. 2023. Opportunities and challenges of ChatGPT for design knowledge management. Procedia CIRP 119: 21–28. https://doi.org/10.1016/j.procir.2023.05.001
Huang J., Chen X., Mishra S., Zheng H. S., Yu A. W. et al. 2023. Large language models cannot self-correct reasoning yet. arXiv preprint. https://doi.org/10.48550/arXiv.2310.01798
Inkinen H. 2016. Review of empirical research on knowledge management practices and firm performance. Journal of Knowledge Management 20 (2): 230–257. https://doi.org/10.1108/JKM-09-2015-0336
Jablonka K., Ai Q., Al-Feghali A., Badhwar S., Bocarsly J. et al. 2023. 14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon. Digital Discovery 2 (5): 1233–1250. https://doi.org/10.1039/D3DD00113J
Jarrahi M., Askay D., Eshraghi A., Smith P. 2023. Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons 66 (1): 87–99. https://doi.org/10.1016/j.bushor.2022.03.002
Jiang S., Hu J., Magee C. L., Luo J. 2024. Deep learning for technical document classification. IEEE Transactions on Engineering Management 71: 1163–1179. https://doi.org/10.1109/TEM.2022.3152216
Jiang Z., Xu F. F., Araki J., Neubig G. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics 8: 423–438. https://doi.org/10.1162/tacl_a_00324
Kampik T., Warmuth C., Rebmann A., Agam R., Egger L. et al. 2023. Large process models: business process management in the age of generative AI. arXiv preprint. https://doi.org/10.48550/arXiv.2309.00900
Kandpal N., Deng H., Roberts A., Wallace E., Raffel C. 2023. Large language models struggle to learn long-tail knowledge. In: Proceedings of the 40th International Conference on Machine Learning, PMLR 202: 15696–15707. https://proceedings.mlr.press/v202/kandpal23a.html
Lan Y., He G., Jiang J., Jiang J., Zhao W. X., Wen J. R. 2022. Complex knowledge base question answering: A survey. IEEE Transactions on Knowledge and Data Engineering 35 (11): 11196–11215. https://doi.org/10.1109/TKDE.2022.3223858
Lewis P., Perez E., Piktus A., Petroni F., Karpukhin V. et al. 2020. Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). https://doi.org/10.48550/arXiv.2005.11401
Li C., Wang J., Zhang Y., Zhu K., Hou W. et al. 2023. Large language models understand and can be enhanced by emotional stimuli. arXiv preprint. https://doi.org/10.48550/arXiv.2307.11760
Li J., Wang S., Zhang M., Li W., Lai Yu. et al. 2024. Agent hospital: A simulacrum of hospital with evolvable medical agents. arXiv preprint. https://doi.org/10.48550/arXiv.2405.02957
Liga D., Robaldo L. 2023. Fine-tuning GPT-3 for legal rule classification. Computer Law & Security Review 51: 105864. https://doi.org/10.1016/j.clsr.2023.105864
Ling C., Zhao X., Lu J., Deng C., Zheng C. et al. 2023. Domain specialization as the key to make large language models disruptive: A comprehensive survey. arXiv preprint. https://doi.org/10.48550/arXiv.2305.18703
Lipsey R. G., Carlaw K. I., Bekar C. T. 2005. Economic Transformations: General Purpose Technologies and Long-Term Economic Growth. Oxford University Press.
Lopez-Lira A., Tang Y. 2023. Can СhatGPT forecast stock price movements? Return predictability and large language models. arXiv preprint. https://doi.org/10.48550/arXiv.2304.07619
Manesh M., Pelligrini M., Marzi G., Dabic M. 2021. Knowledge management in the fourth industrial revolution: Mapping the literature and scoping future avenues. IEEE Transactions on Engineering Management 68 (1): 289–300. https://doi.org/10.1109/TEM.2019.2963489
Maslej N., Fattorini L., Perrault R., Parli V., Reuel A. et al. 2024. The AI index 2024 annual report. Institute for Human-Centered AI, Stanford University. [Electronic resource]. https://aiindex.stanford.edu/report/ (accessed: 23.04.2024).
Mikalef P., Gupta M. 2021. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management 58 (3): 103434. https://doi.org/10.1016/j.im.2021.103434
Mohr J., Yunus A. 2023. Generative AI: What it means for knowledge management. Forrester Report. [Electronic resource]. https://www.forrester.com/report/generative-ai-what-it-means-for-knowledge-management/RES179524 (accessed: 19.04.2024).
Nonaka I., Takeuchi H. 2019. The Wise Company: How Companies Create Continuous Innovation. Oxford University Press.
Noy S., Zhang W. 2023. Experimental evidence on the productivity effects of generative artificial intelligence. Science 381 (6654): 187–192. https://doi.org/10.1126/science.adh2586
O’Leary D. E. 2024. The rise and design of enterprise large language models. IEEE Intelligent Systems 39 (1): 60–63. https://doi.org/10.1109/MIS.2023.3345591
Pan S., Luo L., Wang Y., Chen C., Wang J., Wu X. 2024. Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering 36 (7): 3580–3599. https://doi.org/10.1109/TKDE.2024.3352100
Peifeng L. I. U., Qian L., Zhao X., Tao B. 2024. Joint Knowledge Graph and Large Language Model for Fault Diagnosis and Its Application in Aviation Assembly. IEEE Transactions on Industrial Informatics 20 (6): 8160–8169. https://doi.org/10.1109/TII.2024.3366977
Platt M., Platt D. 2023. Effectiveness of generative artificial intelligence for scientific content analysis. In: IEEE 17th International Conference on Application of Information and Communication Technologies (AICT); 1–4. https://doi.org/10.1109/AICT59525.2023.10313167
Radford A., Narasimhan K., Salimans T., Sutskever I. 2018. Improving language understanding by generative pre-training. [Electronic resource]. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf (accessed: 09.04.2024).
Rethlefsen M. L., Kirtley Sh., Waffenschmidt S., Ayala A. P., Moher D. et al. 2021. PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews. Systematic Reviews 10: 39. https://doi.org/10.1186/s13643-020-01542-z
Ritala P., Ruokonen M., Ramaul L. 2024. Transforming boundaries: How does ChatGPT change knowledge work? Journal of Business Strategy 45 (3): 214–220. https://doi.org/10.1108/JBS-05-2023-0094
Roberts A., Raffel C., Shazeer N. 2020. How much knowledge can you pack into the parameters of a language model? arXiv preprint. https://doi.org/10.48550/arXiv.2002.08910
Romera-Paredes B., Barekatain M., Novikov A., Balog M., Kumar M. P. et al. 2024. Mathematical discoveries from program search with large language models. Nature 625 (7995): 468–475. https://doi.org/10.1038/s41586-023-06924-6
Sadiq R. B., Safie N., Abd Rahman A. H., Goudarzi S. 2021. Artificial intelligence maturity model: A systematic literature review. PeerJ Computer Scence 7: e661. https://doi.org/10.7717/peerj-cs.661
Sahoo P., Singh A., Saha S., Jain V., Mondal S., Chadha A. 2024. A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv preprint. https://doi.org/10.48550/arXiv.2402.07927
Saka A., Taiwo R., Saka N., Salami B. A., Ajayi S. et al. 2024. GPT models in construction industry: Opportunities, limitations, and a use case validation. Developments in the Built Environment 17: 100300. https://doi.org/10.1016/j.dibe.2023.100300
Sarkar C., Das B., Rawat V. S., Wahlang J. B., Nongpiur A. et al. 2023. Artificial intelligence and machine learning technology driven modern drug discovery and development. International Journal of Molecular Sciences 24 (3): 2026. https://doi.org/10.3390/ijms24032026
Sauer P., Seuring S. 2023. How to conduct systematic literature reviews in management research: A guide in 6 steps and 14 decisions. Review of Managerial Science 17: 1899–1933. https://doi.org/10.1007/s11846-023-00668-3
Schaeffer R., Miranda B., Koyejo S. 2023. Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems (NeurIPS 2023) 36. [Electronic resource]. https://proceedings.neurips.cc/paper_files/paper/2023/hash/adc98a266f45005c403b8311ca7e8bd7-Abstract-Conference.html (accessed: 18.04.2024).
Shanahan M. 2024. Talking about large language models. Communications of the ACM 67 (2): 68–79. https://doi.org/10.1145/3624724
Snyder H. 2019. Literature review as a research methodology: An overview and guidelines. Journal of Business Research 104: 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Sun K., Xu Y. E., Zha H., Liu Y., Dong X. L. 2023. Head-to-tail: How knowledgeable are large language models (LLM)? AKA will LLMs replace knowledge graphs? arXiv preprint. https://doi.org/10.48550/arXiv.2308.10168
Sun Y., Zhang Q., Bao J., Lu Y., Liu S. 2024. Empowering digital twins with large language models for global temporal feature learning. Journal of Manufacturing Systems 74: 83–99. https://doi.org/10.1016/j.jmsy.2024.02.015
Sutton R. 2019. The Bitter Lesson. Incomplete Ideas (blog). [Electronic resource]. http://www.incompleteideas.net/IncIdeas/BitterLesson.html (accessed: 11.05.2024).
Taylor R., Kardas M., Cucurull G., Scialom T., Hartshorn A. et al. 2022. Galactica: A large language model for science. arXiv preprint. https://doi.org/10.48550/arXiv.2211.09085
Teubner T., Flath C. M., Weinhardt C., van der Aalst W., Hinz O. 2023. Welcome to the era of ChatGPT et al. The prospects of large language models. Business & Information Systems Engineering 65 (2): 95–101. https://doi.org/10.1007/s12599-023-00795-x
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L. et al. 2017. Attention is all you need. In: 31st Conference on Neural Information Processing Systems (NIPS 2017). [Electronic resource]. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (accessed: 09.04.2024).
Vinchon F., Bartolotta S., Gironnay V., Botella M., Bourgeois-Bougrine S. et al. 2023. Artificial Intelligence & Creativity: A manifesto for collaboration. Journal of Creative Behavior 57 (4): 472–484. https://doi.org/10.1002/jocb.597
Walkowiak E. 2023. Task-interdependencies between generative AI and workers. Economic Letters 231: 111315. https://doi.org/10.1016/j.econlet.2023.111315
Wang C., Liu X., Song D. 2020. Language models are open knowledge graphs. arXiv preprint. https://doi.org/10.48550/arXiv.2010.11967
Wang K., Variengien A., Conmy A., Shlegeris B., Steinhardt J. 2022. Interpretability in the wild: A circuit for indirect object identification in GPT-2 small. arXiv preprint. https://doi.org/10.48550/arXiv.2211.00593
Wang R., Zelikman E., Poesia G., Pu Y., Haber N., Goodman N. D. 2023. Hypothesis search: Inductive reasoning with language models. arXiv preprint. https://doi.org/10.48550/arXiv.2309.05660
Wang S., Xu S. 2024. 16 Changes to the way enterprises are building and buying generative AI. a16. [Electronic resource]. https://a16z.com/generative-ai-enterprise-2024/ (accessed: 05.04.2024).
Weidinger L., Uesato J., Rauh M., Griffin C., Huang P.-S. et al. 2022. Taxonomy of risks posed by language models. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT’22); 214–229. https://doi.org/10.1145/3531146.3533088
Wu S., Irsoy O., Dabravolski V., Dredze M., Gehrmann S. 2023. BloombergGPT: A large language model for finance. arXiv preprint. https://doi.org/10.48550/arXiv.2303.17564
Xi Z., Chen W., Guo X., He W., Ding Y. et al. 2023. The rise and potential of large language model based agents: A survey. arXiv preprint. https://doi.org/10.48550/arXiv.2309.07864
Yang C., Wang X., Lu Y., Liu H., Le Q. V., Zhou D., Chen X. 2023. Large language models as optimizers. arXiv preprint. https://doi.org/10.48550/arXiv.2309.03409
Yang L., Chen H., Li Z., Ding X., Wu X. 2024a. Give us the facts: Enhancing large language models with knowledge graphs for fact-aware language modeling. IEEE Transactions on Knowledge and Data Engineering. Early Access. https://doi.org/10.1109/TKDE.2024.3360454
Yang J., Jin H., Tang R., Han X., Feng Q. et al. 2024b. Harnessing the power of LLMs in practice: A survey on ChatGPT and beyond. ACM Transactions on Knowledge Discovery from Data. https://doi.org/10.1145/3649506
Yang T., Mei Y., Xu L., Yu H., Chen Y. 2024c. Application of question answering systems for intelligent agriculture production and sustainable management: A review. Resources, Conservation and Recycling 204: 107497. https://doi.org/10.1016/j.resconrec.2024.107497
Yao S., Yu D., Zhao J., Shafran I., Griffiths T. et al. 2023. Tree of thoughts: Deliberate problem solving with large language models. In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). [Electronic resource]. https://proceedings.neurips.cc/paper_files/paper/2023/hash/271db9922b8d1f4dd7aaef84ed5ac703-Abstract-Conference.html (accessed: 02.05.2024).
Yu D., Zhu C., Yang Y., Zeng M. 2022a. JAKET: Joint pre-training of knowledge graph and language understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-22) 36 (10): 11630–11638. https://doi.org/10.1609/aaai.v36i10.21417
Yu W., Zhu C., Li Z., Hu Z., Wang Q. et al. 2022b. A survey of knowledge-enhanced text generation. ACM Computing Surveys 54 (11s): 1–38. https://doi.org/10.1145/3512467
Yue X., Ni Y., Zhang K., Zheng T., Liu R. et al. 2023. MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI. arXiv preprint. https://doi.org/10.48550/arXiv.2311.16502
Zelenkov Y. 2018. The effectiveness of Russian organizations: The role of knowledge management and change readiness. Russian Management Journal 16 (4): 513–536. https://doi.org/10.21638/spbu18.2018.403
Zelenkov Y. 2022. Explaining the IT value through the information support of decision-making. In: Digitalization of Society, Economics and Management: A Digital Strategy Based on Post-pandemic Developments. Springer; 29-48. https://doi.org/10.1007/978-3-030-94252-6_3
Zhong Y., Goodfellow S. D. 2024. Domain-specific language models pre-trained on construction management systems corpora. Automation in Construction 160: 105316. https://doi.org/10.1016/j.autcon.2024.105316
Zhou B., Li X., Liu T., Xu K., Liu W., Bao J. 2024. CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics 59: 102333. https://doi.org/10.1016/j.aei.2023.102333
Translation of references in Russian into English
Dolotov A. 2024. Integration difficulties: What will help accelerate GPT business transformation. Forbes. [Electronic resource]. https://www.forbes.ru/mneniya/507501-sloznosti-integracii-cto-pomozet-uskorit-gpt-transformaciu-biznesa (accessed: 05.04.2024). (In Russian)
Yakov and Partners. 2023. Artificial intelligence in Russia — 2023: Trends and prospects. [Electronic resource]. https://yakovpartners.ru/publications/ai-future/ (accessed: 05.04.2024). (In Russian)
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2024-12-29
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Zelenkov, Y. A. (2024). Knowledge management in organization and the large language models. Russian Management Journal, 22(3), 573–601. https://doi.org/10.21638/spbu18.2024.309
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Critical Surveys
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Articles of the Russian Management Journal are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.
