Logically Consistent Language Models via Neuro-Symbolic Integration

• URL: http://arxiv.org/abs/2409.13724v1
• Date: Mon, 9 Sep 2024 10:52:57 GMT
• Title: Logically Consistent Language Models via Neuro-Symbolic Integration
• Authors: Diego Calanzone, Stefano Teso, Antonio Vergari,
• Abstract summary: Large language models (LLMs) are a promising venue for natural language understanding and generation. LLMs are prone to generating non-factual information and to contradicting themselves when prompted to reason about relations between entities of the world. We introduce a loss based on neuro-symbolic reasoning that teaches an LLM to be logically consistent with an external set of facts and rules.
• Score: 14.317886666902822
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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