Towards Logically Consistent Language Models via Probabilistic Reasoning
<|reference_start|>Towards Logically Consistent Language Models via Probabilistic Reasoning: 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.<|reference_end|>
@article{calanzone2024towards,
    title={Towards Logically Consistent Language Models via Probabilistic Reasoning},
    author={Diego Calanzone, Stefano Teso, Antonio Vergari},
    journal={arXiv preprint arXiv:2404.12843},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12843},
    primaryClass={cs.LG cs.CL}
}