{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "ganguli-et-al-human-redteaming-2022", "title": "Ganguli and colleagues: human red-teaming methods and scaling observations", "summary": "Collected human adversarial conversations with language models and compared model size and training approaches, documenting how red-teaming outcomes, participant behavior and observed harms depend on the evaluation process.", "boundary": "A participant-generated dataset is not an unbiased estimate of real-world harm prevalence. 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detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Eli Tran-Johnson", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Dario Amodei", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Tom Brown", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } } ], "origin": "HUMAN_LEGACY", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "A participant-generated dataset is not an unbiased estimate of real-world harm prevalence. Safety findings depend on recruitment, incentives, model access and rating procedures; human red-teaming complements rather than certifies deployment safety.", "domain": "Language-model safety evaluation" }, "sources": [ { "citation": "Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned", "url": "https://arxiv.org/abs/2209.07858v1" } ], "statement": "# Ganguli and colleagues: human red-teaming methods and scaling observations\n\nCollected human adversarial conversations with language models and compared model size and training approaches, documenting how red-teaming outcomes, participant behavior and observed harms depend on the evaluation process.\n\n## Scientific scope\n\nA participant-generated dataset is not an unbiased estimate of real-world harm prevalence. Safety findings depend on recruitment, incentives, model access and rating procedures; human red-teaming complements rather than certifies deployment safety.\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\nDeep Ganguli; Liane Lovitt; Jackson Kernion; Amanda Askell; Yuntao Bai; Saurav Kadavath; Ben Mann; Ethan Perez; Nicholas Schiefer; Kamal Ndousse; Andy Jones; Sam Bowman; Anna Chen; Tom Conerly; Nova DasSarma; Dawn Drain; Nelson Elhage; Sheer El-Showk; Stanislav Fort; Zac Hatfield Dodds; Tom Henighan; Danny Hernandez; Tristan Hume; Josh Jacobson; Scott Johnston; Shauna Kravec; Catherine Olsson; Sam Ringer; Eli Tran-Johnson; Dario Amodei; Tom Brown; Nicholas Joseph; Sam McCandlish; Chris Olah; Jared Kaplan; Jack Clark\n\nSource-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.\n\n\n\n## Original sources\n\n- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://arxiv.org/abs/2209.07858v1)\n", "summary": "Collected human adversarial conversations with language models and compared model size and training approaches, documenting how red-teaming outcomes, participant behavior and observed harms depend on the evaluation process.", "title": "Ganguli and colleagues: human red-teaming methods and scaling observations" }, "record_id": "urn:uuid:9bec4cdd-159e-4cac-a81b-60e17b10324d", "version": 1, "source_ref": { "record_id": "urn:uuid:9bec4cdd-159e-4cac-a81b-60e17b10324d", "version": 1, "content_hash": "sha256:6bcc4ee0d960eca9d89b815feec3c549412269fd976aeb9aaf53e914756c56d1" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:869e3bdb36060180074c6211185405588ea8065c74b0253840da8608ca78e3a1" }, "target": { "record_id": "urn:uuid:9bec4cdd-159e-4cac-a81b-60e17b10324d", "version": 1, "content_hash": "sha256:6bcc4ee0d960eca9d89b815feec3c549412269fd976aeb9aaf53e914756c56d1" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "ganguli-et-al-human-redteaming-2022", "entry_id": "ganguli-et-al-human-redteaming-2022", "revision": 1, "title": "Ganguli and colleagues: human red-teaming methods and scaling observations", "context": "Language-model safety evaluation", "track": "Reusable scientific method, experimental result or historical framework", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-23", "evidence_cutoff": "2026-10-23", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Collected human adversarial conversations with language models and compared model size and training approaches, documenting how red-teaming outcomes, participant behavior and observed harms depend on the evaluation process.", "scope": "Collected human adversarial conversations with language models and compared model size and training approaches, documenting how red-teaming outcomes, participant behavior and observed harms depend on the evaluation process. A participant-generated dataset is not an unbiased estimate of real-world harm prevalence. Safety findings depend on recruitment, incentives, model access and rating procedures; human red-teaming complements rather than certifies deployment safety.", "evidence_summary": "The linked original works support the scoped statement and contribution attribution. WRS has not independently replayed experiments or reconstructed a full mathematical proof. A participant-generated dataset is not an unbiased estimate of real-world harm prevalence. 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