Multimodal large language models (MLLMs), which combine text and visual data processing, enhance the ability of artificial intelligence to understand and interact with the world. However, most open-source MLLMs are limited in their ability to process complex visual inputs and support multiple languages which can hinder their practical application.
A research collaboration from several Chinese institutions…
Physics-Informed Neural Networks (PINNs), a blend of deep learning with physical laws, are increasingly used to resolve complex differential equations and signify a considerable leap in scientific computing and applied mathematics. The uniqueness of PINNs lies in embedding differential equations directly into the structure of neural networks, thus ensuring the adherence of solutions to fundamental…
The development and progress in the field of artificial intelligence (AI) are unending, with the recent emergence of the AI model, "gpt2-chatbot", generating significant interest within AI circles on Twitter. This model, known as a large language model (LLM), has incited considerable exploration and curiosity amongst AI developers and enthusiasts, who are constantly searching to…
French researchers have developed the first publicly available benchmark tool, 'DrBenchmark', to evaluate and standardize evaluation protocols for pre-trained masked language models (PLMs) in French, particularly in the biomedical field. Existing models lacked standardized protocols and comprehensive datasets, leading to inconsistent results and stalling progress in natural language processing (NLP) research.
The advent and advancement…
In the field of computational linguistics, large amounts of text data present a considerable challenge for language models, especially when specific details within large datasets need to be identified. Several models, like LLaMA, Yi, QWen, and Mistral, use advanced attention mechanisms to deal with long-context information. Techniques such as continuous pretraining and sparse upcycling help…
Emerging research from the New York University's Center for Data Science asserts that language models based on transformers play a key role in driving AI forward. Traditionally, these models have been used to interpret and generate human-like sequences of tokens, a fundamental mechanism used in their operational framework. Given their wide range of applications, from…