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Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.
Forward citations
Cited by 4 Pith papers
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A new maze-navigation benchmark for LLMs reports that reasoning models outperform standard ones, but the link from performance gaps to a lack of persistent self-awareness is an overreach.
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Linear Spatial World Models Emerge in Large Language Models
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MA-CBP: A Criminal Behavior Prediction Framework Based on Multi-Agent Asynchronous Collaboration
MA-CBP is a proposed multi-agent AI system that turns live video into text descriptions and summaries and reasons jointly to warn about potential criminal behavior.
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