Category: Technology - Page 2
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.
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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.
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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.
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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.
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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.
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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.
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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.
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How to Detect Fabricated References in Large Language Model Outputs
Fabricated references from AI models are slipping into real research papers. Learn how to detect them, why they happen, and what institutions must do to stop them before science loses its foundation.
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Accessibility Regulations for Generative AI: WCAG Compliance and Assistive Features
Generative AI must follow WCAG accessibility standards just like human-created content. Learn how to comply with legal requirements, avoid lawsuits, and build inclusive AI systems that work for everyone.
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How Combining RAG with Decoding Strategies Improves LLM Accuracy
Combining RAG with advanced decoding strategies like Layer Fused Decoding and entropy-based weighting drastically reduces LLM hallucinations. This approach grounds responses in live data while guiding word-by-word generation for higher accuracy.
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Life Sciences Research with Generative AI: Protein Design and Literature Reviews
Generative AI is revolutionizing life sciences by designing entirely new proteins for medicine and industry-beyond what nature evolved. From cancer therapies to plastic-eating enzymes, this is how AI is reshaping biology.
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GPU Selection for LLM Inference: A100 vs H100 vs CPU Offloading
H100 GPUs now outperform A100s and CPU offloading for LLM inference, offering faster responses, lower cost per token, and better scalability. Choose H100 for production, A100 only for small models, and avoid CPU offloading for real-time apps.
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