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While language models (LMs) can sometimes generate factually correct text andestimate truth values of individual claims, these generally do not reflect aglobally coherent, manipulable model of the world. As a consequence, currentLMs also generate incorrect or nonsensical content, and are difficult to editand bring up to date. We present a method called Deductive Closure Training(DCT) that uses LMs themselves to identify implications of (and contradictionswithin) the text that they generate, yielding an efficient self-supervisedprocedure for improving LM factuality. Given a collection of seed documents,DCT prompts LMs to generate additional text implied by these documents, reasonglobally about the correctness of this generated text, and finally fine-tune ontext inferred to be correct. Given seed documents from a trusted source, DCTprovides a tool for supervised model updating; if seed documents are sampledfrom the LM itself, DCT enables fully unsupervised fine-tuning for improvedcoherence and accuracy. Across the CREAK, MQUaKE, and Reversal Curse datasets,supervised DCT improves LM fact verification and text generation accuracy by3-26%; on CREAK fully unsupervised DCT improves verification accuracy by 12%.These results show that LMs' reasoning capabilities during inference can beleveraged during training to improve their reliability.

Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability