Results 21 to 30 of about 2,402,901 (306)

Neural Natural Language Inference Models Enhanced with External Knowledge

open access: yes, 2018
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-
Chen, Qian   +4 more
core   +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

Slim Embedding Layers for Recurrent Neural Language Models

open access: yes, 2017
Recurrent neural language models are the state-of-the-art models for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models. In this paper,
Kulhanek, Raymond   +4 more
core   +1 more source

From Large Language Models to Large Multimodal Models: A Literature Review

open access: yesApplied Sciences
With 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

Stereotactic Body Radiation Therapy for Pediatric, Adolescent, and Young Adult Patients With Osteosarcoma: Local Control Outcomes With Dosimetric Analysis

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background/Objectives Osteosarcoma is a radioresistant tumor that may benefit from stereotactic body radiation therapy (SBRT) for locoregional control in metastatic/recurrent disease. We report institutional practice patterns, outcomes, toxicity, and failures in osteosarcoma patients treated with SBRT.
Jenna Kocsis   +13 more
wiley   +1 more source

Role play with large language models

open access: yesNature, 2023
As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. In this paper, we foreground the concept of role-play.
Murray Shanahan   +2 more
openaire   +6 more sources

Changes in Body Composition in Children and Young People Undergoing Treatment for Acute Lymphoblastic Leukemia: A Systematic Review and Meta‐Analysis

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Ongoing evidence indicates increased risk of sarcopenic obesity among children and young people (CYP) with acute lymphoblastic leukemia (ALL), often beginning early in treatment, persisting into survivorship. This review evaluates current literature on body composition in CYP with ALL during and after treatment.
Lina A. Zahed   +5 more
wiley   +1 more source

Language Models for Everyone—Responsible and Transparent Development of Open Large Language Models

open access: yesComputer Sciences & Mathematics Forum, 2023
Large language and multimodal models are revolutionising many aspects of human work and creativity, with broad potential not only as chatbots and for information retrieval but as interaction points and integrators of large technical systems.
Daniel Gillblad
doaj   +1 more source

Parent‐to‐Child Information Disclosure in Pediatric Oncology

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Despite professional consensus regarding the importance of open communication with pediatric cancer patients about their disease, actual practice patterns of disclosure are understudied. Extant literature suggests a significant proportion of children are not told about their diagnosis/prognosis, which is purported to negatively ...
Rachel A. Kentor   +12 more
wiley   +1 more source

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study ...
Severin Kasser   +6 more
wiley   +1 more source

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