Historical milestone · 2020
Language Models are Few-Shot Learners
Reviewed through September 18, 2026
2020 · Historical milestone
Language Models are Few-Shot Learners
- Era
- 2020s
- Theme
- Language models & representation
- Evidence form
- Large-scale model + benchmarks
- School / paradigm
- Autoregressive scaling / in-context learning
- Institution / context
- OpenAI
- Researchers
- Tom Brown; collaborators
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Scaled autoregressive language modeling to 175 billion parameters and evaluated zero-, one-, and few-shot prompting without gradient updates.
Result / historical claim. Made in-context examples a general task interface across many tested language benchmarks.
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Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Large-scale model + benchmarks
Limitation / debate. Contamination, cost, bias, prompt sensitivity, and unreliable reasoning constrained broad claims.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
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