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It does not turn every generated answer into a scientific result or establish reliable general intelligence. The pinned paper is v4; no extra benchmark package is counted.", "domain": "Language modeling" }, "sources": [ { "citation": "Language Models are Few-Shot Learners", "url": "https://arxiv.org/abs/2005.14165v4" } ], "statement": "# GPT-3 and in-context few-shot language tasks\n\nThe GPT-3 paper studied a large autoregressive language model performing many tasks from instructions and examples in its prompt, without task-specific gradient updates.\n\n## Scientific scope\n\nThis is a human-authored model and evaluation contribution. It does not turn every generated answer into a scientific result or establish reliable general intelligence. The pinned paper is v4; no extra benchmark package is counted.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nTom B. Brown; Benjamin Mann; Nick Ryder; Melanie Subbiah; Jared Kaplan; Prafulla Dhariwal; Arvind Neelakantan; Pranav Shyam; Girish Sastry; Amanda Askell; Sandhini Agarwal; Ariel Herbert-Voss; Gretchen Krueger; Tom Henighan; Rewon Child; Aditya Ramesh; Daniel M. Ziegler; Jeffrey Wu; Clemens Winter; Christopher Hesse; Mark Chen; Eric Sigler; Mateusz Litwin; Scott Gray; Benjamin Chess; Jack Clark; Christopher Berner; Sam McCandlish; Alec Radford; Ilya Sutskever; Dario Amodei\n\nSource paper byline retained, including repeated label positions. Names are not verified unique identities or measurements of individual effort.\n\n\n\n## Original sources\n\n- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165v4)\n", "summary": "The GPT-3 paper studied a large autoregressive language model performing many tasks from instructions and examples in its prompt, without task-specific gradient updates.", "title": "GPT-3 and in-context few-shot language tasks" }, "record_id": "urn:uuid:dfcedc8d-f7c6-4d75-abc0-af25097b5444", "version": 1, "source_ref": { "record_id": "urn:uuid:dfcedc8d-f7c6-4d75-abc0-af25097b5444", "version": 1, "content_hash": "sha256:5cdc4c72d82acd246a63dde8bb672754a78cb09278e67219221bb2bbc6b00cd5" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:e7de0907a691cab9fe20e85d1ef7315b2e7b07a3e4bc8dac80431a33fa38a1a4" }, "target": { "record_id": "urn:uuid:dfcedc8d-f7c6-4d75-abc0-af25097b5444", "version": 1, "content_hash": "sha256:5cdc4c72d82acd246a63dde8bb672754a78cb09278e67219221bb2bbc6b00cd5" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "gpt3-few-shot", "entry_id": "gpt3-few-shot", "revision": 1, "title": "GPT-3 and in-context few-shot language tasks", "context": "Language modeling", "track": "Scoped scientific or research-capability contribution", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-09-14", "evidence_cutoff": "2026-09-14", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "The GPT-3 paper studied a large autoregressive language model performing many tasks from instructions and examples in its prompt, without task-specific gradient updates.", "scope": "The GPT-3 paper studied a large autoregressive language model performing many tasks from instructions and examples in its prompt, without task-specific gradient updates. Scope limitation: This is a human-authored model and evaluation contribution. It does not turn every generated answer into a scientific result or establish reliable general intelligence. The pinned paper is v4; no extra benchmark package is counted.", "evidence_summary": "Assessment uses the catalogue-linked source evidence for the stated contribution. This is a human-authored model and evaluation contribution. It does not turn every generated answer into a scientific result or establish reliable general intelligence. 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