Getting situated: Comparative Analysis of Language models with experimental categorization tasks

Edginer, A., & Goldstone, R. L. (2022). Getting situated: Comparative Analysis of Language models with experimental categorization tasks.  Proceedings of the 44th Annual Conference of the Cognitive Science Society. (pp. 230-236).  Toronto, Canada. Cognitive Science Society.

Common critiques of natural language processing (NLP) methods cite their lack of multimodal sensory information, claiming an inability to learn situated, action-oriented relations through language alone. Barsalou’s (1983) theory of ad hoc categories, which are formed from to achieve goals in real-world scenarios, correspond theoretically to those types of relations with which language models ought to have great difficulty. Recent NLP models have developed dynamic approaches to word representations, where the same word can have different encodings depending on the context in which it appears. Testing these models using categorization tasks with human response data demonstrates that situated properties may be partially captured through semantic analysis. We discuss possible ways in which different notions of situatedness may be distinguished for future development and testing of NLP models.

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