In the field of artificial intelligence, the evaluation of Large Language Models (LLMs) poses significant challenges; particularly with regard to data adequacy and the quality of a model’s free-text output. One common solution is to use a singular large LLM, like GPT-4, to evaluate the results of other LLMs. However, this methodology has drawbacks, including…
Large Language Models (LLMs) represent a significant advancement across several application domains, delivering remarkable results in a variety of tasks. Despite these benefits, the massive size of LLMs renders substantial computational costs, making them challenging to adapt to specific downstream tasks, particularly on hardware systems with limited computational capabilities.
With billions of parameters, these models…
Generative artificial intelligence's (AI) ability to create new text, images, videos, and other media represents a huge technological advancement. However, there's a downside: generative AI may unwittingly infrive on copyrights by using existing creative works as raw material without the original author's consent. This poses serious economic and legal challenges for content creators and creative…
Large language models (LLMs) are increasingly in use, which is leading to new cybersecurity risks. The risks stem from their main characteristics: enhanced capability for code creation, deployment for real-time code generation, automated execution within code interpreters, and integration into applications handling unprotected data. It brings about the need for a strong approach to cybersecurity…
Artificial Intelligence (AI) is significantly transforming the healthcare industry, addressing challenges in areas such as diagnostics and treatment planning. Large Language Models (LLMs) are emerging as a revolutionary tool in this sector, capable of deciphering and understanding complex health data. However, the intricate nature of medical data and the need for accuracy and efficiency in…
Proximal Policy Optimization (PPO), initially designed for continuous control tasks, is widely used in reinforcement learning (RL) applications, like fine-tuning generative models. However, PPO's effectiveness is based on a series of heuristics for stable convergence, like value networks and clipping, adding complexities in its implementation.
Adapting PPO to optimize complex modern generative models with billions of…
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…
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…