{"schema_version":"onlylabs.public_signal.v1","title":"Anthropic Writing: Measuring Progress On Scalable Oversight For Large Language Models","description":"Anthropic writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/e74820dc-ef32-4c82-82f4-36ec00cdd7c7","json_url":"https://onlylabs.fyi/signals/e74820dc-ef32-4c82-82f4-36ec00cdd7c7/signal.json","generated_at":"2026-06-11T04:18:00.872757+00:00","org":{"slug":"anthropic","name":"Anthropic","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/anthropic","dossier_json_url":"https://onlylabs.fyi/labs/anthropic/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/e74820dc-ef32-4c82-82f4-36ec00cdd7c7","signal_json":"https://onlylabs.fyi/signals/e74820dc-ef32-4c82-82f4-36ec00cdd7c7/signal.json","source":"https://www.anthropic.com/research/measuring-progress-on-scalable-oversight-for-large-language-models","lab_dossier":"https://onlylabs.fyi/labs/anthropic","lab_dossier_json":"https://onlylabs.fyi/labs/anthropic/dossier.json","analysis":"https://onlylabs.fyi/analysis/anthropic","analysis_json":"https://onlylabs.fyi/analysis/anthropic/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/anthropic/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"Anthropic published Measuring Progress On Scalable Oversight For Large Language Models. 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Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on ways it can be studied empirically. We first present an experimental design centered on tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight..."},"evidence_pages":[{"url":"https://www.anthropic.com/research/measuring-progress-on-scalable-oversight-for-large-language-models","final_url":"https://www.anthropic.com/research/measuring-progress-on-scalable-oversight-for-large-language-models","title":"Measuring Progress On Scalable Oversight For Large Language Models","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T04:18:00.872757+00:00","bytes":106502,"raw_path":"72d83671b5f929a1026ac58a803592ab4b5765a5f37b8ee6c498c1cd0b1ee8bf.html","content_hash":"7acb03c83017830bb65f772ec1494ad6b0ad65e8c13f5ff1ceb056db3d84bf83","excerpt_chars":1200,"truncated":true,"excerpt":"Measuring Progress on Scalable Oversight for Large Language Models \\ Anthropic Alignment Research Measuring Progress on Scalable Oversight for Large Language Models Nov 4, 2022 Read Paper Abstract Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on ways it can be studied empirically. We first present an experimental design centered on tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. 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