Reasoning skills of large language models are often overestimated

Pre-trained language models (LM), by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are *dilettantes* rather than domain experts – they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances... See more

LeCun points to four essential characteristics of human intelligence that current AI systems, including LLMs, can’t replicate: reasoning, planning, persistent memory, and understanding the physical world. He stresses that LLMs’ reliance on textual data severely limits their understanding of reality: “We’re easily fooled into thinking they are intel... See more

The question of whether LLMs can reason is, in many ways, the wrong question. The more interesting question is whether they are limited to memorization / interpolative retrieval, or whether they can adapt to novelty beyond what they know. (They can't, at least until you start doing active inference, or using them in a search loop, etc.) There are ... See more

Language Models Represent Space and Time , Wes Gurnee and Max Tegmark argue that “modern LLMs acquire structured knowledge about fundamental dimensions such as space and time, supporting the view that they learn not merely superficial statistics, but literal world models”, based on some analyses of the alleged capacity of LLMs to understand geograp... See more

LLMs struggle when handling tasks which require extensive knowledge. This limitation highlights the need to supplement LLMs with non-parametric knowledge. This paper Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts analyze the effects of different types of non-parametric knowledge, such as textu... See more