Towards Logically Consistent Language Models via Probabilistic Reasoning

• URL: http://arxiv.org/abs/2404.12843v1
• Date: Fri, 19 Apr 2024 12:23:57 GMT
• Title: Towards Logically Consistent Language Models via Probabilistic Reasoning
• Authors: Diego Calanzone, Stefano Teso, Antonio Vergari,
• Abstract summary: Large language models (LLMs) are a promising venue for natural language understanding and generation tasks. LLMs are prone to generate non-factual information and to contradict themselves when prompted to reason about beliefs of the world. We introduce a training objective that teaches a LLM to be consistent with external knowledge in the form of a set of facts and rules.
• Score: 14.317886666902822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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