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

arXiv:2401.08574v2 Announce Type: replace
Abstract: 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 …

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