Towards Hybrid Reasoning: Assimilating Structure into Subsymbolic Systems
Artificial intelligence software was used to enhance the grammar, flow, and readability of this article’s text.
The recent advances in large language models (LLMs) have demonstrated their remarkable fluency and adaptability when generating text. After exposure to just a few examples, these models can produce surprisingly coherent continuations on a wide array of topics, exhibiting signs of flexible understanding and reasoning.
However, further analysis reveals fundamental gaps that temper unbridled optimism. At their core, LLMs accumulate only statistical patterns reflecting correlations of terms in their massive training corpus. They lack any structured representations or explicit modeling of concepts, relationships or rules. As a result, they frequently make basic logical errors, fail at systematic generalization beyond their training distribution, and struggle to sustain coherent reasoning chains.
Their fundamental nature as pattern recognizers driven by data frequencies rather than deeper understanding renders them brittle whenever queries require nuanced inference, causal analysis or compositional generalization. Questions demanding multi-step deduction, unraveling dynamics or synthesis of disjoint facts readily expose these limitations.
In contrast, knowledge graphs provide structured symbolic representations by explicitly encoding concepts as interlinked nodes within a network. Rich semantics are…
