Text-JEPA: A Joint Embedding Predictive Architecture for the Conversion of Natural Language into First-Order Logic
Abstract: The conversion of knowledge from natural language (NL) into first-order logic (FOL) plays a crucial role in Knowledge Representation and Cognitive Science, particularly in the context of hybrid Neural-Symbolic models. This process not only facilitates logical knowledge modeling but also enhances the effectiveness of language models and symbolic reasoning systems. The binary nature of FOL—where statements are either true or false—ensures greater consistency and efficiency in these models.
External IDs:dblp:conf/iccci/LeTNHPBQB25
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