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History, development, and principles of large language models: an introductory survey
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“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]
Citations
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“Initially, considering a text of length T , the most basic approach of positional encoding is counting.”
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×
“These aspects disproportionately affect already disadvantaged and marginalized groups [15, 22, 162].”
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Unsupervised Cross-lingual Representation Learning at Scale
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LLaMA: Open and Efficient Foundation Language Models
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Vitkovskyi, D. O., & Shymkovych, V. (2026) · PROBLEMS IN PROGRAMMING · doi:10.15407/pp2026.03.053
Man, S. S., Wu, X., Tong, M., & Chan, A. H. S. (2026) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2026.104072
Priske, J., & Dutt, P. K. (2026) · TalTech Journal of European Studies · doi:10.2478/bjes-2026-0029
Metadata
- 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
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