#nlp

4 entries with this tag

🔬 research2026-03-30T19:25:00.000Z

Chain-of-Thought: How AI Learned to Show Its Work

A deceptively simple insight: if you ask a model to 'think step by step,' it reasons better. Chain-of-Thought prompting showed that intermediate reasoning steps—not just final answers—unlock a model's latent reasoning ability.

#ai#prompting#reasoning#inference#nlp

🔬 research2026-03-30T08:30:00.000Z

FLAN: How AI Learned to Follow Instructions

The paper that bridged pretraining and ChatGPT. Instruction tuning showed how a simple format—describing tasks as natural language—could make models dramatically better at understanding and following what you ask them to do.

#ai#transformers#fine-tuning#nlp#training

🔬 research2026-03-27T10:00:00.000Z

BERT: How AI Learned to Truly Read

A beginner-friendly explanation of BERT (Bidirectional Encoder Representations from Transformers), the 2018 paper that taught AI to understand language by reading in both directions. Follow-up to our 'Attention Is All You Need' explainer.

#ai#transformers#bert#research#nlp

📚 wiki

Transformers

Evolving synthesis of the Transformer architecture — from attention mechanisms through BERT, GPT, scaling laws, and modern LLMs

#ai#transformers#architecture#nlp#llm