GPT-3 (175B) shows in-context few-shot learning
OpenAI's 175-billion-parameter GPT-3 could perform new tasks from a few examples in its prompt, without fine-tuning; it was offered via the OpenAI API from June 2020.
Key facts
- Paper 'Language Models are Few-Shot Learners', arXiv 2005.14165 (28 May 2020)
- 175 billion parameters, ~10x larger than any previous dense LM
- Trained on ~300B tokens
- OpenAI API launched in private beta on 11 June 2020
- NeurIPS 2020 best paper award
What happened
GPT-3 demonstrated that scale alone yielded 'in-context learning' across translation, QA, arithmetic and writing.
Why it matters
Turned LLMs into a platform; many startups were built on its API, and it directly preceded InstructGPT and ChatGPT.
Changelog
- 2026-09-29: created
Related events
- OpenAI publishes 'Scaling Laws for Neural Language Models' ★★★★★
- InstructGPT: RLHF aligns language models to follow instructions ★★★★★
- OpenAI launches ChatGPT ★★★★★
- OpenAI announces GPT-2 and withholds the full model over misuse concerns ★★★★
- Microsoft invests $1 billion in OpenAI ★★★
- GitHub Copilot and OpenAI Codex bring LLMs to programming ★★★★
Sources (2)
id: 2020-05-28-gpt-3 · updated 2026-09-29 · open in the interactive timeline