Category: Technology - Page 3

Training Data Poisoning Risks for Large Language Models and How to Mitigate Them

Training data poisoning lets attackers silently corrupt AI models with tiny amounts of fake data. Learn how it works, real-world examples, and the six proven ways to defend your LLMs.

In-Context Learning in Large Language Models: How LLMs Learn from Prompts Without Training

In-context learning lets large language models perform new tasks just by seeing examples in prompts-no training needed. Discover how it works, why it's replacing fine-tuning, and how to use it effectively.

Scientific Workflows with Large Language Models: How Hypotheses and Methods Are Changing Research

Large language models are transforming scientific research by automating literature reviews, generating hypotheses, and designing experiments. But they come with serious risks-hallucinations, errors, and overreliance. Learn how Sci-LLMs work, where they excel, and how to use them safely.

Long-Context Risks in Generative AI: Distortion, Drift, and Lost Salience

Long-context AI models can process massive amounts of text, but they struggle with distortion, drift, and lost salience-especially in the middle of documents. Learn how these risks undermine reliability and what’s being done to fix them.

Transparency and Explainability in Large Language Model Decisions

Transparency and explainability in large language models are critical for trust and fairness. Without knowing how decisions are made, AI risks reinforcing bias and eroding public trust - especially in high-stakes areas like finance and healthcare.

Citations and Sources in Large Language Models: What They Can and Cannot Do

LLMs can generate convincing citations-but most are fake. Learn why AI hallucinates sources, how often they get it wrong, and how to use them safely without trusting their references.

Pretraining Objectives in Generative AI: Masked Modeling, Next-Token Prediction, and Denoising

Masked modeling, next-token prediction, and denoising are the three core pretraining methods powering today’s generative AI. Each excels in different tasks-from understanding text to generating images. Learn how they work, where they shine, and why hybrid approaches are the future.

How Training Duration and Token Counts Affect LLM Generalization

Training duration and token counts don't guarantee better LLM generalization. What matters is how sequence lengths are structured during training. Learn why variable-length training beats raw scale and how to avoid common pitfalls.

Multi-Agent Systems with LLMs: How Specialized AI Agents Collaborate to Solve Complex Problems

Multi-agent systems with LLMs use specialized AI agents working together to solve complex tasks better than any single model. Learn how frameworks like Chain-of-Agents, MacNet, and LatentMAS enable collaboration, role specialization, and efficiency gains.

How to Detect Fabricated References in Large Language Model Outputs