{"schema_version":"onlylabs.public_signal.v1","title":"Anthropic Writing: Language Models Mostly Know What They Know","description":"Anthropic writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/ad601fd0-5fd9-43b5-9e0f-530bef28f5f9","json_url":"https://onlylabs.fyi/signals/ad601fd0-5fd9-43b5-9e0f-530bef28f5f9/signal.json","generated_at":"2026-06-11T04:18:06.667826+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/ad601fd0-5fd9-43b5-9e0f-530bef28f5f9","signal_json":"https://onlylabs.fyi/signals/ad601fd0-5fd9-43b5-9e0f-530bef28f5f9/signal.json","source":"https://www.anthropic.com/research/language-models-mostly-know-what-they-know","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 Language Models Mostly Know What They Know. This talking signal gives public context for research themes, product direction, policy, or launch framing. 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self-evaluation on open-ended sampling tasks by asking models to first propose answers, and then to evaluate the probability \"P(True)\" that their answers are correct. We find encouraging performance, calibration, and scaling for P(True) on a diverse array of tasks. Performance at self-evaluation further improves when we allow models to consider many of their own samples before predicting the validity of one specific possibility. Next, we investigate whether models can be trained to predict \"P(IK)\", the probability that \"I know\" the answer to a question, without reference to any particular proposed answer. Models perform well at predicting P(IK) and partially generalize across tasks, though they struggle with calibration..."},"evidence_pages":[{"url":"https://www.anthropic.com/research/language-models-mostly-know-what-they-know","final_url":"https://www.anthropic.com/research/language-models-mostly-know-what-they-know","title":"Language Models Mostly Know What They Know","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T04:18:06.667826+00:00","bytes":106224,"raw_path":"670397a4d6a661381461398ef3dde0266d391eac75f865588679f7442eec5146.html","content_hash":"ae3bf4b1fbc9eb1c385dab7d58d90fbd18599ed59453b2da73391da81174313b","excerpt_chars":1200,"truncated":true,"excerpt":"Language Models (Mostly) Know What They Know \\ Anthropic Alignment Research Language Models (Mostly) Know What They Know Jul 11, 2022 Read Paper Abstract We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self-evaluation on open-ended sampling tasks by asking models to first propose answers, and then to evaluate the probability \"P(True)\" that their answers are correct. We find encouraging performance, calibration, and scaling for P(True) on a diverse array of tasks. Performance at self-evaluation further improves when we allow models to consider many of their own samples before predicting the validity of one specific possibility. Next, we investigate whether models can be trained to predict \"P(IK)\", the probability that \"I know\" the answer to a question, without reference to any particular proposed answer. 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