Results 11 to 20 of about 520,319 (211)

OffensEval 2023: Offensive language identification in the age of Large Language Models [PDF]

, 2023
The OffensEval shared tasks organized as part of SemEval-2019–2020 were very popular, attracting over 1300 participating teams. The two editions of the shared task helped advance the state of the art in offensive language identification by providing the ...
Ranasinghe, Tharindu, Nakov, Preslav, Zampieri, Marcos, Dmonte, Alphaeus, Rosenthal, Sara   +4 more
core   +1 more source

Medical education empowered by generative artificial intelligence large language models [PDF]

, 2023
Generative artificial intelligence (GAI) large language models (LLMs), like ChatGPT, have become the world's fastest growing applications. Here, we provide useful strategies for educators in medical and health science (M&HS) to integrate GAI-LLMs ...
Todorovic, Michael, Singleton, R, Jessica Stokes-Parish, Stokes-Parish, Jessica, Todorovic, M, Stokes-Parish, J, Michael Todorovic, Jowsey, Tanisha, Singleton, Rachelle, Tanisha Jowsey, Rachelle Singleton, Jowsey, T   +11 more
core   +1 more source

Leveraging Computational Psychometrics for Language Testing [PDF]

, 2023
The recent surge in the popularity of Large Language Models (LLM) for language assessment underscores the growing significance of cost-effective language evaluation in our increasingly digitalized society.
Ardeshir Geranpayeh
core   +1 more source

Integrating Graphs with Large Language Models: Methods and Prospects [PDF]

, 2023
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more.
Zheng, Yizhen, Pan, Shirui, Liu, Yixin
core   +2 more sources

Leveraging Large Language Models for Predictive Chemistry [PDF]

, 2023
Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine-learning approaches that incorporate ...
Andres , Ortega-Guerrero, Kevin Maik, Jablonka, Philippe , Schwaller, Berend, Smit   +3 more
core   +1 more source

Validity of Large Language Models for Sentiment Analysis: Evidence of performance comparable to human coders [PDF]

, 2023
Assessing the sentiment of content is a major focus of communication science. Previous research has compared the performance of ‘gold-standard’ methods – exemplified by trained human coders – with approaches such as crowd-sourcing, data dictionaries, and
James Elsey
core   +1 more source

Large Language Models as Optimizers

CoRR, 2023
ICLR 2024; 42 pages, 26 figures, 15 tables.
Chengrun Yang, Xuezhi Wang 0002, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen   +6 more
openaire   +3 more sources

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

A Watermark for Large Language Models

CoRR, 2023
Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models.
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein   +5 more
openaire   +3 more sources

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