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Tag: large language models

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Jul, 30 2026

How Large Language Models Use Probabilities to Choose Words and Phrases

Emily Fies
0
Explore how Large Language Models use conditional probability, softmax functions, and decoding strategies like top-p and temperature to choose words. Learn why this statistical approach leads to both impressive creativity and occasional hallucinations.
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Jul, 18 2026

Causal vs Bidirectional Attention: Tradeoffs in Modern LLMs

Emily Fies
7
Explore the critical tradeoffs between causal and bidirectional attention in modern LLMs. Learn how these mechanisms impact performance, speed, and suitability for different AI tasks.
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Jun, 28 2026

What Makes a Language Model 'Large': Beyond Parameter Counts and Into Capabilities

Emily Fies
5
Explore what truly makes a language model 'large' in 2026. From emergent capabilities to Virtual Logical Depth, discover why parameter counts no longer define AI performance.
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Jun, 22 2026

Hybrid Recurrent-Transformer Models: Do They Actually Help LLMs?

Emily Fies
0
Explore how hybrid recurrent-transformer models combine Mamba and attention to solve LLM scaling issues. Learn about sequential vs. parallel designs, real-world examples like Hunyuan-TurboS, and performance trade-offs.
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Jun, 10 2026

Semantic Search with LLMs: How AI Transforms Keyword Matching into Intent Understanding

Emily Fies
0
Discover how Large Language Models transform search from keyword matching to intent understanding. Learn about vector embeddings, query expansion, and re-ranking strategies for building smarter, semantic search systems.
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Mar, 27 2026

Robustness and Generalization Tests for Large Language Model Reliability

Emily Fies
7
Learn essential robustness testing methods for LLMs beyond standard benchmarks, including adversarial stress tests, OOD validation, and real-world deployment readiness.
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Feb, 24 2026

Long-Form Generation with Large Language Models: How to Keep Structure, Coherence, and Facts Accurate

Emily Fies
7
Long-form generation with large language models can produce detailed content, but structure, coherence, and facts often break down. Learn how to guide AI for reliable long-form output using outlines, RAG, and human review.
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Feb, 2 2026

How Curriculum and Data Mixtures Speed Up Large Language Model Scaling

Emily Fies
5
Smart data ordering and mixtures can boost LLM performance by up to 15% without larger models. Learn how curriculum learning works, what mixtures to use, and whether it’s worth the effort for your team.
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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
6
Transformers revolutionized AI by letting language models understand context instantly. Learn how self-attention, positional encoding, and multi-head attention power today’s top LLMs - and why they’re replacing older models.
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Jan, 23 2026

Why Transformers Power Modern Large Language Models: The Core Concepts You Need

Emily Fies
9
Transformers revolutionized AI by enabling large language models to understand context across long texts using self-attention. This article explains how they work, why they beat older models, and what’s changing in 2025.
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Jan, 17 2026

Domain Adaptation in NLP: How to Fine-Tune Large Language Models for Medical, Legal, and Financial Text

Emily Fies
9
Learn how to fine-tune large language models for medical, legal, and financial text using domain adaptation. Discover methods, costs, pitfalls, and real-world results.
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Jan, 7 2026

Structured vs Unstructured Pruning for Efficient Large Language Models

Emily Fies
5
Structured and unstructured pruning help shrink large language models for faster, cheaper deployment. Structured pruning works on any device; unstructured offers higher compression but needs special hardware. Here's how to choose the right one.

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