AIMC Topic: Language

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A shape composition method for named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific informat...

Enhancing text-centric fake news detection via external knowledge distillation from LLMs.

Neural networks : the official journal of the International Neural Network Society
Fake news poses a significant threat to society, making the automatic and accurate detection of fake news an urgent task. Various detection cues have been explored in extensive research, with news text content shown to be indispensable as it directly...

Comparative Analysis of Information Quality in Pediatric Otorhinolaryngology: Clinicians, Residents, and Large Language Models.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Pediatric otorhinolaryngology (ORL) addresses complex conditions in children, requiring a tailored approach for patients and families. With artificial intelligence (AI) gaining traction in medical applications, this study evaluates the qua...

SAF: An action framework to self-check the Understanding Self-Consistency of Large Language Models.

Neural networks : the official journal of the International Neural Network Society
Large Language Models (LLMs), which are trained on massive text data, have demonstrated remarkable advancements in language understanding capabilities. Nevertheless, it remains unclear to what extent LLMs have effectively captured and utilized the im...

Towards zero-shot human-object interaction detection via vision-language integration.

Neural networks : the official journal of the International Neural Network Society
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. Such heavy...

PanoGen++: Domain-adapted text-guided panoramic environment generation for vision-and-language navigation.

Neural networks : the official journal of the International Neural Network Society
Vision-and-language navigation (VLN) tasks require agents to navigate three-dimensional environments guided by natural language instructions, offering substantial potential for diverse applications. However, the scarcity of training data impedes prog...

Evaluating the Accuracy, Reliability, Consistency, and Readability of Different Large Language Models in Restorative Dentistry.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
OBJECTIVE: This study aimed to evaluate the reliability, consistency, and readability of responses provided by various artificial intelligence (AI) programs to questions related to Restorative Dentistry.

Exploiting instance-label dynamics through reciprocal anchored contrastive learning for few-shot relation extraction.

Neural networks : the official journal of the International Neural Network Society
In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models...

Primer on large language models: an educational overview for intensivists.

Critical care (London, England)
The integration of artificial intelligence (AI) and machine learning-enabled medical technologies into clinical practice is expanding at an unprecedented pace. Among these, large language models (LLMs) represent a subset of machine learning designed ...

The benefits and dangers of anthropomorphic conversational agents.

Proceedings of the National Academy of Sciences of the United States of America
A growing body of research suggests that the recent generation of large language model (LLMs) excel, and in many cases outpace humans, at writing persuasively and empathetically, at inferring user traits from text, and at mimicking human-like convers...