
Logically consistent large language models can be achieved by fine-tuning them using a principled neuro-symbolic reasoning approach that encourages the model to satisfy a given set of logical constraints.


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improving-logical-consistency-of-large-language-models-via-neuro-symbolic-integration


Improving Logical Consistency of Large Language Models via Neuro-Symbolic Integration


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Large language models can be trained to be more logically consistent and factual by incorporating principled probabilistic reasoning into the training objective, without relying on external reasoning tools.


improving-logical-consistency-and-factuality-of-large-language-models-through-probabilistic-reasoning


Improving Logical Consistency and Factuality of Large Language Models through Probabilistic Reasoning
