Logically Consistent Language Models via Neuro-Symbolic Integration
<|reference_start|>Logically Consistent Language Models via Neuro-Symbolic Integration: Large language models (LLMs) are a promising venue for natural language understanding and generation. However, current LLMs are far from reliable: they are prone to generating non-factual information and, more crucially, to contradicting themselves when prompted to reason about relations between entities of the world. These problems are currently addressed with large scale fine-tuning or by delegating reasoning to external tools. In this work, we strive for a middle ground and introduce a loss based on neuro-symbolic reasoning that teaches an LLM to be logically consistent with an external set of facts and rules and improves self-consistency even when the LLM is fine-tuned on a limited set of facts. Our approach also allows to easily combine multiple logical constraints at once in a principled way, delivering LLMs that are more consistent w.r.t. all constraints and improve over several baselines w.r.t. a given constraint. Moreover, our method allows LLMs to extrapolate to unseen but semantically similar factual knowledge, represented in unseen datasets, more systematically.<|reference_end|>
@article{calanzone2024logically,
    title={Logically Consistent Language Models via Neuro-Symbolic Integration},
    author={Diego Calanzone, Stefano Teso, Antonio Vergari},
    journal={arXiv preprint arXiv:2409.13724},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13724},
    primaryClass={cs.CL cs.AI cs.LG}
}