  While language models (LMs) can sometimes generate factually correct text and
estimate truth values of individual claims, these generally do not reflect a
globally coherent, manipulable model of the world. As a consequence, current
LMs also generate incorrect or nonsensical content, and are difficult to edit
and bring up to date. We present a method called Deductive Closure Training
(DCT) that uses LMs themselves to identify implications of (and contradictions
within) the text that they generate, yielding an efficient self-supervised
procedure for improving LM factuality. Given a collection of seed documents,
DCT prompts LMs to generate additional text implied by these documents, reason
globally about the correctness of this generated text, and finally fine-tune on
text inferred to be correct. Given seed documents from a trusted source, DCT
provides a tool for supervised model updating; if seed documents are sampled
from the LM itself, DCT enables fully unsupervised fine-tuning for improved
coherence and accuracy. Across the CREAK, MQUaKE, and Reversal Curse datasets,
supervised DCT improves LM fact verification and text generation accuracy by
3-26%; on CREAK fully unsupervised DCT improves verification accuracy by 12%.
These results show that LMs' reasoning capabilities during inference can be
leveraged during training to improve their reliability.
