Large language models face challenges like hallucinations, complex reasoning, and planning under uncertainty. Logical discrete graphical models offer a solution by providing structured reasoning capabilities. The article discusses the relationship between theorem-proving and computation, highlighting the importance of logical fragments like Horn Clauses. It also explores different levels of graphical structures and their applications in logical reasoning. The issue of hallucination in large language models is addressed, emphasizing the need for causality-aware models to prevent unreliable outputs. Various existing solutions like discriminative fine-tuning and retrieval-augmented generation are compared with logical graphical models for information synthesis.
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by Gregory Copp... at arxiv.org 03-15-2024
https://arxiv.org/pdf/2403.09599.pdfDeeper Inquiries