Given the emergent reasoning abilities of large language models, information retrieval is becoming more complex. Rather than just retrieve a document, modern information retrieval systems advertise that they can synthesize an answer based on potentially many different documents, conflicting data sources, and using reasoning. We review recent literature and argue that the large language model has crucial flaws that prevent it from on its own ever constituting general intelligence, or answering general information synthesis requests. This review shows that the following are problems for large language models: hallucinations, complex reasoning, planning under uncertainty, and complex calculations. We outline how logical discrete graphical models can solve all of these problems, and outline a method of training a logical discrete model from unlabeled text.

翻译：鉴于大型语言模型展现出的涌现推理能力，信息检索正变得愈发复杂。现代信息检索系统宣称不仅能检索文档，还能基于潜在的多份不同文档、冲突的数据源以及推理能力综合生成答案。本文通过回顾近期文献，论证大型语言模型存在关键缺陷，使其无法独立构成通用智能或应对通用信息综合请求。本综述表明，以下问题对大型语言模型构成挑战：幻觉现象、复杂推理、不确定性下的规划以及复杂计算。我们概述了逻辑离散图模型如何解决所有这些问题，并提出一种从无标签文本中训练逻辑离散模型的方法。