Towards Logically Consistent Language Models via Probabilistic Reasoning

Authors: Diego Calanzone, Antonio Vergari, Stefano Teso

Project URL: https://openreview.net/forum?id=q3SGbfj19d

Paper: https://openreview.net/pdf?id=q3SGbfj19d

Abstract: Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. However, current LLMs are far from reliable: they are prone to generate non-factual information and, more crucially, to contradict themselves when prompted to reason about beliefs of the world. These problems are currently addressed with large scale fine-tuning or by delegating consistent reasoning to external tools. In this work, we strive for a middle ground and introduce a training objective based on principled probabilistic reasoning that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules. Fine-tuning with our loss on a limited set of facts enables our LLMs to be more logically consistent than previous baselines and allows them to extrapolate to unseen but semantically similar factual knowledge more systematically.

@inproceedings{calanzone2024locolm, title={Towards Logically Consistent Language Models via Probabilistic Reasoning}, author={Diego Calanzone, Antonio Vergari, Stefano Teso}, booktitle={ICLR 2024 Workshop on Reliable and Responsible Foundation Models}, year={2024} }