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Large Language Models in Ophthalmology: A Bibliographic Analysis [PDF]

Türk Oftalmoloji Dergisi
This study evaluated the distribution of research on the use of large language models (LLMs) in ophthalmology through a bibliographic analysis of articles retrieved from PubMed through November 2024.
Neslihan Dilruba Köseoğlu, T.Y. Alvin Liu   +1 more
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Knowledge Engineering Using Large Language Models [PDF]

Transactions on Graph Data and Knowledge, 2023
Knowledge engineering is a discipline that focuses on the creation and maintenance of processes that generate and apply knowledge. Traditionally, knowledge engineering approaches have focused on knowledge expressed in formal languages.
Allen, Bradley P., Stork, Lise, Groth, Paul   +2 more
doaj   +1 more source

Large Language Models are Not Models of Natural Language: They are Corpus Models

IEEE Access, 2022
Natural Language Processing (NLP) has become one of the leading application areas in the current Artificial Intelligence boom. Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve ...
Csaba Veres
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Large Language Models and Logical Reasoning

Encyclopedia, 2023
In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts.
Robert Friedman
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Large language models and political science

Frontiers in Political Science, 2023
Large Language Models (LLMs) are a type of artificial intelligence that uses information from very large datasets to model the use of language and generate content.
Mitchell Linegar, Rafal Kocielnik, R. Michael Alvarez, R. Michael Alvarez   +3 more
doaj   +1 more source

Can large language models write reflectively

Computers and Education: Artificial Intelligence, 2023
Generative Large Language Models (LLMs) demonstrate impressive results in different writing tasks and have already attracted much attention from researchers and practitioners. However, there is limited research to investigate the capability of generative
Yuheng Li, Lele Sha, Lixiang Yan, Jionghao Lin, Mladen Raković, Kirsten Galbraith, Kayley Lyons, Dragan Gašević, Guanliang Chen   +8 more
doaj   +1 more source

Consistent Responses to Paraphrased Questions as Evidence Against Hallucination: A Study on Hallucinations in LLMs [PDF]

International Journal of Web Research
The increasing adoption of large language models (LLMs) has intensified concerns about hallucinations—outputs that are syntactically fluent but factually incorrect.
Tara Zare, Mehrnoush Shamsfard
doaj   +1 more source

Enhancing Agriculture QA Models Using Large Language Models [PDF]

BIO Web of Conferences
Agriculture is a complex process that requires a great deal of knowledge and experience. Yet, the complexity and often chaotic nature of web data presents a considerable challenge in obtaining suitable results.
He Xiaoyan
doaj   +1 more source

Emotional intelligence of Large Language Models

Journal of Pacific Rim Psychology, 2023
Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning.
Xuena Wang, Xueting Li, Zi Yin, Yue Wu, Jia Liu   +4 more
doaj   +1 more source

AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development

Journal of Advanced Research
Background: Due to the recent revolution of artificial intelligence (AI), AI-enabled large language models (LLMs) have flourished and started to be applied in various sectors of science and medicine.
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Srijan Chatterjee, Arpita Das, Sang-Soo Lee   +5 more
doaj   +1 more source