Results 11 to 20 of about 239,466 (252)

Introduction to Large Language Models (LLMs) for dementia care and research [PDF]

open access: yesFrontiers in Dementia
IntroductionDementia is a progressive neurodegenerative disorder that affects cognitive abilities including memory, reasoning, and communication skills, leading to gradual decline in daily activities and social engagement.
Matthias S. Treder   +3 more
doaj   +2 more sources

Performance of large language models (LLMs) in providing prostate cancer information [PDF]

open access: yesBMC Urology
Purpose The diagnosis and management of prostate cancer (PCa), the second most common cancer in men worldwide, are highly complex. Hence, patients often seek knowledge through additional resources, including AI chatbots such as ChatGPT and Google Bard ...
Ahmed Alasker   +7 more
doaj   +2 more sources

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs [PDF]

open access: yesSIGKDD Explorations, 2023
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text ...
Zhikai Chen   +10 more
semanticscholar   +1 more source

Recommender Systems in the Era of Large Language Models (LLMs) [PDF]

open access: yesIEEE Transactions on Knowledge and Data Engineering, 2023
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an indispensable and important component, providing personalized suggestions that cater to user preferences.
Wenqi Fan   +7 more
semanticscholar   +1 more source

Label-free Node Classification on Graphs with Large Language Models (LLMS) [PDF]

open access: yesInternational Conference on Learning Representations, 2023
In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance.
Zhikai Chen   +7 more
semanticscholar   +1 more source

Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs? [PDF]

open access: yesNorth American Chapter of the Association for Computational Linguistics, 2023
Since the recent prosperity of Large Language Models (LLMs), there have been interleaved discussions regarding how to reduce hallucinations from LLM responses, how to increase the factuality of LLMs, and whether Knowledge Graphs (KGs), which store the ...
Kai Sun   +4 more
semanticscholar   +1 more source

Several categories of Large Language Models (LLMs): A Short Survey [PDF]

open access: yesInternational Journal for Research in Applied Science and Engineering Technology, 2023
: Large Language Models (LLMs) have become effective tools for natural language process-ing and have been used in many different fields. This essay offers a succinct summary of various LLM subcategories.
Saurabh Pahune, M. Chandrasekharan
semanticscholar   +1 more source

Large language models encode clinical knowledge [PDF]

open access: yesNature, 2022
Med-PaLM, a state-of-the-art large language model for medicine, is introduced and evaluated across several medical question answering tasks, demonstrating the promise of these models in this domain.
K. Singhal   +29 more
semanticscholar   +1 more source

A Survey on Evaluation of Large Language Models [PDF]

open access: yesACM Transactions on Intelligent Systems and Technology, 2023
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes
Yu-Chu Chang   +15 more
semanticscholar   +1 more source

The potential of Large Language Models in language education

open access: yesОсвітній вимір, 2021
This editorial explores the potential of Large Language Models (LLMs) in language education. It discusses the role of LLMs in machine translation, the concept of ‘prompt programming’, and the inductive bias of LLMs for abstract textual reasoning.
Vita A. Hamaniuk
doaj   +1 more source

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