Transaction Details

Transaction Hash
0xf0001ed4b5b1485a44eb5d0d2490dd421ad9a0d3698230eecb436ba0c94cc3c9
Block
10176
Timestamp
Feb 19, 2026, 06:50:06 AM
Nonce
20
Operation Type
SET

Operation

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        "topic_path": "ai/transformers/decoder-only",
        "title": "Language Models are Few-Shot Learners (GPT-3)",
        "content": "# Language Models are Few-Shot Learners (GPT-3) (2020)\n\n## Authors\nBrown, Mann, Ryder, Subbiah, Kaplan, Dhariwal, Neelakantan, Shyam, Sastry, Askell, et al.\n\n## Paper\nhttps://arxiv.org/abs/2005.14165\n\n## Code\nN/A (API only)\n\n## Key Concepts\n- In-context learning\n- Few-shot prompting\n- Scaling laws for language models\n\n## Builds On\n- Language Models are Unsupervised Multitask Learners (GPT-2)\n\n## Influenced\n- LLaMA: Open and Efficient Foundation Language Models\n\n## Summary\nScaled to 175B parameters and demonstrated that very large language models exhibit strong few-shot and in-context learning abilities, achieving competitive results on many NLP benchmarks without gradient updates.",
        "summary": "Scaled to 175B parameters and demonstrated that very large language models exhibit strong few-shot and in-context learning abilities, achieving competitive results on many NLP benchmarks without gradient updates.",
        "depth": 2,
        "tags": "decoder-only,autoregressive,few-shot,in-context-learning,large-scale,builds-on:gpt2",
        "price": null,
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        "content_hash": null,
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        "title": "Language Models are Few-Shot Learners (GPT-3)",
        "depth": 2,
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