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- W4385644019 abstract "A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning. In this article, we delve into the performance of Large Language Models (LLMs) in dealing with progressively complex analogies expressed in unstructured text. We discuss analogies at four distinct levels of complexity: lexical analogies, syntactic analogies, semantic analogies, and pragmatic analogies. As the analogies become more complex, they require increasingly extensive, diverse knowledge beyond the textual content, unlikely to be found in the lexical co-occurrence statistics that power LLMs. To address this, we discuss the necessity of employing Neuro-symbolic AI techniques that combine statistical and symbolic AI, informing the representation of unstructured text to highlight and augment relevant content, provide abstraction and guide the mapping process. Our knowledge-informed approach maintains the efficiency of LLMs while preserving the ability to explain analogies for pedagogical applications." @default.
- W4385644019 created "2023-08-08" @default.
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- W4385644019 date "2023-08-02" @default.
- W4385644019 modified "2023-10-05" @default.
- W4385644019 title "Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?" @default.
- W4385644019 doi "https://doi.org/10.48550/arxiv.2308.01936" @default.
- W4385644019 hasPublicationYear "2023" @default.
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