A large language model (LLM) is an AI system trained on massive amounts of text that generates and understands natural language by predicting the most likely next word in a sequence.
A large language model, or LLM, is a type of neural network trained on huge collections of text — books, articles, code, and web pages — to predict what word (or part of a word) comes next in a sequence. That simple prediction task, repeated across billions of examples, is enough to teach the model grammar, facts, reasoning patterns, and writing style.
Most LLMs are built on the transformer architecture, which lets the model weigh how relevant every other word in a passage is to the word it's currently generating. Training happens in two stages: pretraining, where the model learns general language patterns from a broad corpus, and fine-tuning, where it's adjusted for a specific behavior like following instructions or refusing harmful requests.
LLMs power chatbots, coding assistants, search summarization, translation, and content generation. Because they generate text one token at a time based on probability, they can also produce a hallucination — stating something confidently that isn't true — which is why systems built on them are often paired with retrieval-augmented generation or other grounding techniques.
LLMs don't "know" facts the way a database does — they reproduce patterns learned during training, which is why they can be outdated, biased toward their training data, or wrong about niche topics. Their knowledge is also bounded by a context window: the amount of text they can consider at once.
A new arXiv study evaluates ten AI-driven security tools, identifies common operational failures in LLM-based penetration testing, and introduces a four-dimensional Integration Friction Index to help practitioners build more reliable automated security pipelines.
DeepSQL warns that while connecting a large language model (LLM) to a production database is straightforward, revoking that access is surprisingly difficult because LLMs retain learned data. This creates persistent security and compliance risks for companies using AI tools.
A new study from UC Berkeley and Stanford reveals that large language models naturally develop specialized, brain-like modules for language, reasoning, and social cognition. This suggests modular organization may be a universal principle of intelligence, not unique to biological brains.
ThoughtDAG is an open-source tool by Chen Xiachan that lets users visually edit the context of LLM conversations as a Directed Acyclic Graph (DAG), enabling transparent and customizable AI interactions.
A new arXiv study warns that large language models (LLMs) are not yet safe for autonomous clinical decision support, despite passing medical licensing exams. The research highlights critical risks like misdiagnosis and lack of contextual understanding when AI is used without human oversight in real-world patient triage.