Tag: large language models
What Makes a Language Model 'Large': Beyond Parameter Counts
Discover why parameter count is no longer the best metric for defining 'large' language models. Learn about emergent capabilities, Virtual Logical Depth, and the true cost of scale.
- Oct 26, 2026
- Collin Pace
- 6
- Permalink
Transfer Learning in NLP: How Pretraining Powers LLM Breakthroughs
Discover how transfer learning revolutionized NLP by enabling pretraining on massive datasets. Learn how models like BERT and GPT-3 use this technique to achieve breakthroughs in language understanding with less data.
- Oct 23, 2026
- Collin Pace
- 0
- Permalink
Emergent Capabilities in Generative AI: The Truth Behind the Magic
Explore the mystery of emergent capabilities in generative AI. Learn why large models suddenly gain new skills and what this means for the future.
- Oct 4, 2026
- Collin Pace
- 0
- Permalink
Encoder-Decoder vs Decoder-Only Transformers: Which Architecture Wins for LLMs?
Discover the critical differences between encoder-decoder and decoder-only transformers. Learn why GPT dominates chat while T5 rules translation, and choose the right LLM architecture for your needs.
- Sep 17, 2026
- Collin Pace
- 7
- Permalink
Compositional Generalization in NLP: Can LLMs Reason Systematically?
Discover why LLMs struggle with compositional generalization. Learn how benchmarks like SCAN and CFQ reveal the gap between memorization and systematic reasoning, and explore practical strategies to improve AI reliability.
- Sep 14, 2026
- Collin Pace
- 6
- Permalink
Prompt Injection in LLMs: Attacks, Defenses & Best Practices
Discover how prompt injection threatens LLMs, why it differs from SQL injection, and effective defense strategies like context partitioning and output validation.
- Aug 31, 2026
- Collin Pace
- 9
- Permalink
Self-Supervised Learning in NLP: How LLMs Learn Without Labels
Explore how self-supervised learning enables large language models to master language without human labels, driving the evolution of NLP and generative AI.
- Jul 20, 2026
- Collin Pace
- 9
- Permalink
Scaling Laws in NLP: How Bigger Data and Models Created Modern LLMs
Explore how scaling laws transformed AI development. Learn how Chinchilla scaling and compute predictions enabled the rise of modern Large Language Models.
- Jul 19, 2026
- Collin Pace
- 0
- Permalink
How Think-Tokens Change Generation: Reasoning Traces in Modern LLMs
Explore how think-tokens and reasoning traces transform LLM generation. We analyze the trade-offs between accuracy and latency, compare major models, and provide strategies for optimizing reasoning in 2026.
- Jul 10, 2026
- Collin Pace
- 0
- Permalink
From BERT to GPT: How LLM Architectures Evolved
Explore the architectural differences between BERT and GPT. Learn how encoder-only and decoder-only designs shape modern AI tasks.
- Jun 21, 2026
- Collin Pace
- 10
- Permalink
Knowledge vs Fluency in Large Language Models: Understanding Strengths and Gaps
Explore the critical gap between LLM fluency and true knowledge. Learn why models like GPT-4 pass exams yet lack deep linguistic understanding, and how to use AI effectively despite these limitations.
- Jun 19, 2026
- Collin Pace
- 0
- Permalink
In-Context Learning Explained: How LLMs Adapt to Prompts Without Retraining
Discover how In-Context Learning enables LLMs to adapt to new tasks via prompts without retraining. Learn the mechanics, best practices, and limitations of few-shot learning.
- Jun 10, 2026
- Collin Pace
- 5
- Permalink
- 1
- 2