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The article announces the GPT‑3.5 Turbo API, highlights its ten‑fold lower price of $0.002 per 1k tokens, explains the new chat‑completion format with messages, and provides a practical Node.js example along with various application ideas.
This article dissects how ChatGPT acquired its surprising capabilities by tracing the evolution from the original GPT‑3 model through instruction tuning, code‑based pre‑training, and reinforcement learning from human feedback, ultimately presenting a comprehensive technical roadmap for reproducing GPT‑3.5‑scale models.
This article examines how OpenAI's GPT‑3.5 series evolved from the original GPT‑3 through large‑scale pre‑training, instruction tuning, code training, and RLHF, detailing the origins of language generation, world knowledge, in‑context learning, code understanding, complex reasoning, and the trade‑offs introduced by alignment.
This article analyses how ChatGPT and the GPT‑3.5 series evolved from the original GPT‑3 through large‑scale pre‑training, code‑based training, instruction tuning, and reinforcement learning from human feedback, identifying the origins of their language generation, in‑context learning, world knowledge, code understanding, chain‑of‑thought reasoning, and alignment capabilities while also outlining current limitations.