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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]
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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×
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Retrieval-Augmented Large Language Models for Computer Architecture Learning and Design Assistance
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Tool or Companion? Reframing Conversational AI to Prevent Psychological Harm
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Exploring Large Language Models' Responses to Moral Reasoning DilemmasPreprint
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MASON - A Multi-Agent LLM Framework for No-Code Development
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CABENCH: Benchmarking Composable Ai for Solving Complex Tasks through Composing Ready-to-Use Models
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RAG-Enhanced LLM for Evaluating Grid Energy Storage Hosting Capacity
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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
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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