architektura: Large language model

Evidence: 100 • authority: 72

A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts. They are the basis for many modern chatbots, such as ChatGPT, Claude, Gemini, Grok, Qwen and DeepSeek. LLMs are typically based on the transformer neural network architecture. Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word. GPTs are then often fine-tuned to follow instructions and to behave as assistants. Biased or inaccurate training data can make an LLM's output less relia…

Evidence: 54 • authority: 72

In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data such as text, images, or audio is converted to a sequence of numerical representations called tokens, and each token is converted into a vector through an embedding layer. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Because self-attention alone is permutation-invariant, transformers inject positional information, typically throu…

Evidence: 54 • authority: 72

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom of the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include c…

Evidence: 45 • authority: 88

Advancing Large Language Model Reasoning Techniques: Methods Enabling LLMs to 'Think' Beyond Text Generation for Reliable and Explainable AI. Large Language Models (LLMs) have revolutionized artificial intelligence applications, extending from writing assistants to Retrieval-Augmented Generation (RAG) systems. However, understanding how LLMs "reason" process complex queries and generate reliable results beyond mere text generation, it has become a pivotal research focus. This paper surveys the core reasoning techniques that empower LLMs to simulate logical thinking: Chain-of-Thought (CoT), Self-Consistency, ReAct (Reason + Act), and Plan-and-Solve Reasoning. We discuss architectural innovat…

Evidence: 45 • authority: 72

A generative pre-trained transformer (GPT) is a type of large language model (LLM) that is widely used in generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large datasets of unlabeled content, and able to generate novel content. OpenAI was the first to apply generative pre-training to the transformer architecture, introducing the GPT-1 model in 2018. The chatbot ChatGPT, released in late 2022 (using GPT-3.5), was followed by many competitor chatbots using their own generative pre-trained transformers to generate text, such as Gemini, DeepSeek and Claude. GPTs are primarily used to generate text, but ca…

Evidence: — • authority: 92

Introduction to Concepts in Artificial Intelligence and Machine Learning for Pharmacoepidemiologists: Large Language Models.. Large language models (LLMs) represent a type of generative artificial intelligence (GenAI) that generate and interpret text, with some LLMs able to process multimodal content (e.g., images, audio, video), and can be deployed as part of agents to perform users' tasks. LLMs can perform natural language processing functions such as summarization, translation, and extraction giving them the potential to enhance and scale pharmacoepidemiological and real-world research by performing tasks such as literature review, data extraction, and medical writing. Despite the growin…

Evidence: — • authority: 88

Topic Driven Text Summarization with Defragmentation using LLMs. Text summarization is an important problem in natural language processing. Summarization of large, complex documents has applications in many domains, such as finance (10K reports, earnings transcripts), customer reviews, healthcare (doctor notes, aggregate patient information), legal and compliance (contracts, court rulings), and academia (scholarly articles, textbooks). A summary can help an investor make better decisions quickly. However, naive summarization of complex documents can be incomplete and may omit important topics from the summary. Unlike documents such as 10k reports, which are required by regulatory compliance…

Evidence: — • authority: 88

Topic Driven Text Summarization with Defragmentation using LLMs. Text summarization is an important problem in natural language processing. Summarization of large, complex documents has applications in many domains, such as finance (10K reports, earnings transcripts), customer reviews, healthcare (doctor notes, aggregate patient information), legal and compliance (contracts, court rulings), and academia (scholarly articles, textbooks). A summary can help an investor make better decisions quickly. However, naive summarization of complex documents can be incomplete and may omit important topics from the summary. Unlike documents such as 10k reports, which are required by regulatory compliance…

Evidence: — • authority: 88

Experimental Evaluation of AI-Augmented Cybersecurity Requirements Generation Leveraging LLMs' Capabilities. When it comes to cybersecurity requirements, catalogs of standard controls are a double-edged sword: they offer reusable resources applicable anywhere, yet they entail tedious, error-prone manual instantiation work. As the definitions of these cybersecurity controls are written in natural language, we analyze whether large language models (LLMs) may help automate that process. We ran an empirical evaluation where frontier versions of LLaMa, Qwen, Mixtral, and GPT were asked, leveraging best prompting practice, to generate cybersecurity requirements for a realistic system, departing f…

Evidence: — • authority: 88

Experimental Evaluation of AI-Augmented Cybersecurity Requirements Generation Leveraging LLMs' Capabilities. When it comes to cybersecurity requirements, catalogs of standard controls are a double-edged sword: they offer reusable resources applicable anywhere, yet they entail tedious, error-prone manual instantiation work. As these cybersecurity controls are textual in nature, we analyze whether large language models (LLMs) may come to help automate that process. We ran an empirical evaluation where frontier versions of LLaMa, QWen, Mixtral, and GPT were asked, leveraging best prompting practice, to generate cybersecurity requirements for a realistic system, departing from its functional sp…

Evidence: — • authority: 72

A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.