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

Authors: Afra Feyza Aky\"urek, Ekin Aky\"urek, Leshem Choshen, Derry Wijaya, Jacob Andreas
Venue: Findings of the Association for Computational Linguistics, {ACL} 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024 (2024)

## 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 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.

## Links
- arXiv: https://arxiv.org/abs/2401.08574
- PDF: https://arxiv.org/pdf/2401.08574
- HTML: https://arxiv.org/html/2401.08574
- Hugging Face: https://huggingface.co/papers/2401.08574
- alphaXiv: https://www.alphaxiv.org/abs/2401.08574
- DOI: https://doi.org/10.48550/ARXIV.2401.08574
- Semantic Scholar: https://www.semanticscholar.org/paper/267028613
- Publisher: https://aclanthology.org/2024.findings-acl.584

## How to cite
@inproceedings{DBLP:conf/acl/AkyurekACWA24,
author       = {Afra Feyza Aky{\"{u}}rek and
                  Ekin Aky{\"{u}}rek and
                  Leshem Choshen and
                  Derry Wijaya and
                  Jacob Andreas},
  editor       = {Lun{-}Wei Ku and
                  Andre Martins and
                  Vivek Srikumar},
  title        = {Deductive Closure Training of Language Models for Coherence, Accuracy,
                  and Updatability},
  booktitle    = {Findings of the Association for Computational Linguistics, {ACL} 2024,
                  Bangkok, Thailand and virtual meeting, August 11-16, 2024},
  pages        = {9802--9818},
  publisher    = {Association for Computational Linguistics},
  year         = {2024},
  url          = {https://aclanthology.org/2024.findings-acl.584},
  timestamp    = {Tue, 27 Aug 2024 17:38:11 +0200},
  biburl       = {https://dblp.org/rec/conf/acl/AkyurekACWA24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}