Zitieren

Bonan Min, H. R. (2023). Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey. ACM Computing SurveysVolume 56(2) -https://doi.org/10.1145/3605943, pp. 1-40. Search in Google Scholar

Harsh Trivedi, N. B. (2023). Questions, Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step. Retrieved 2024, from ArXiv - Computer Science > Computation and Language: https://arxiv.org/abs/2212.10509 Search in Google Scholar

Izzidien, A. (2022). Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments. AI & Society, https://doi.org/10.1007/s00146-021-01167-3, 299–318. Search in Google Scholar

Jacob Devlin, M.-W. C. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 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. 4171–4186). Association for Computational Linguistics. Search in Google Scholar

Jeffrey Pennington, R. S. (2024). GloVe: Global Vectors for Word Representation. Retrieved 2024, from NLP Standford Edu: https://nlp.stanford.edu/projects/glove Search in Google Scholar

Jiaoyan, C. P.-R.-R.-R.-R. (2021). OWL2Vec*: embedding of OWL ontologies. Machine Learning 110, 1813–1845. Search in Google Scholar

Johnson, S. M. (2023). A detailed review on word embedding techniques with emphasis on word2vec. Multimed Tools Appl. https://doi.org/10.1007/s11042-023-17007-z. Search in Google Scholar

LangChain. (2023). Retrieval. Retrieved 2024, from LangChain.com: https://js.langchain.com/docs/modules/data_connection Search in Google Scholar

Lisowski, E. (2023). Best AI Text Analysis Tools Comparison (2023). Retrieved 2024, from Addepto.com: https://addepto.com/blog/best-ai-text-analysis-tools-comparison-2023/ Search in Google Scholar

Madanchian, H. T. (2023). Artificial Intelligence and Sentiment Analysis: A Review in Competitive Research. Computers, 12(2), 37; https://doi.org/10.3390/computers12020037. Search in Google Scholar

Nuthakki, D. R. (2023). Chapter 2 – Natural Language Processing and Text Analytics: Techniques and Applications. Retrieved 2024, from San International - Scientific Publications: https://submissions.nobelonline.in/chapter-2-natural-language-processing-and-text-analytics-techniques-and-applications/ Search in Google Scholar

Ray, P. P. (2023). ChatGPT A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope, ISSN 2667-3452. Internet of Things and Cyber-Physical Systems, 121-154. Search in Google Scholar

Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI Models: A Preliminary Review. Future Internet, 15(6), 192. Search in Google Scholar

SILVA, V. S., FREITAS, A., & HANDSCHUH, S. (2019). On the semantic interpretability of artificial intelligence models. Retrieved from ArXiv preprint: https://arxiv.org/abs/1907.04105 Search in Google Scholar

Wei Jin, A. O. (2010). BERT: a tool for behavioral regression testing. FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering (pp. 361-362). https://doi.org/10.1145/1882291.1882348. Search in Google Scholar

Yang, B. L. (2023). Recent Progress on Text Summarisation Based on BERT and GPT. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. KSEM, Computer Science, vol 14120. Springer, Cham. Search in Google Scholar

Yunfan Gao, Y. X. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. Retrieved 2024, from Arxiv Preprint: https://arxiv.org/abs/2312.10997 Search in Google Scholar

eISSN:
2558-9652
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Wirtschaftswissenschaften, Volkswirtschaft, andere, Betriebswirtschaft, Industrielle Chemie, Energiegewinnung und Umwandlung