Results 81 to 90 of about 6,156,320 (380)
Annals of Clinical and Translational Neurology, Volume 9, Issue 12, Page 1962-1973, December 2022., 2022 Abstract Objectives Early‐ and late‐onset Alzheimer's disease (EOAD and LOAD) share the same neuropathological traits but show distinct cognitive features. We aimed to explore baseline and longitudinal outcomes of global and domain‐specific cognitive function in a well characterized cohort of patients with a biomarker‐based diagnosis.
Adrià Tort‐Merino +16 more
wiley +1 more source
Understanding Telecom Language Through Large Language Models
GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023 <p>The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs ...
Bariah, Lina +5 more
openaire +2 more sources
From Large Language Models to Large Multimodal Models: A Literature Review
Applied SciencesWith the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to
Dawei Huang +3 more
doaj +1 more source
Expert evaluation of large language models for clinical dialogue summarization [PDF]
We assessed the performance of large language models’ summarizing clinical dialogues using computational metrics and human evaluations. The comparison was done between automatically generated and human-produced summaries.
Asif, Nahyan +9 more
core +1 more source
Using Parsimonious Language Models on Web Data [PDF]
, 2008 In this paper we explore the use of parsimonious language models for web retrieval. These models are smaller thus more efficient than the standard language models and are therefore well suited for large-scale web retrieval.
Hiemstra, Djoerd +3 more
core +4 more sources
A Precis of Language Models are not Models of Language [PDF]
arXiv, 2022 Natural Language Processing is one of the leading application areas in the current resurgence of Artificial Intelligence, spearheaded by Artificial Neural Networks. We show that despite their many successes at performing linguistic tasks, Large Neural Language Models are ill-suited as comprehensive models of natural language.
arxiv
On the creativity of large language models
AI & SOCIETYAbstractLarge language models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative?
Franceschelli, Giorgio, Musolesi, Mirco
openaire +2 more sources
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics [PDF]
arXiv, 2023 Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For this reason and because there are no animal models for language, large language models are important in psycholinguistics: they are useful as a practical tool, as an ...
arxiv
Explicitly unbiased large language models still form biased associations [PDF]
Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases. Measuring such implicit biases can be a challenge: As LLMs become increasingly
Bai, Xuechunzi +3 more
core +1 more source
Enhance Reasoning Ability of Visual-Language Models via Large Language Models [PDF]
arXiv, 2023 Pre-trained visual language models (VLM) have shown excellent performance in image caption tasks. However, it sometimes shows insufficient reasoning ability. In contrast, large language models (LLMs) emerge with powerful reasoning capabilities. Therefore, we propose a method called TReE, which transfers the reasoning ability of a large language model ...
arxiv