The Quo Vadis of the Relationship between Language and Large Language Models
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Abstract (arXiv)
In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. While the terminology employed for the characterization of LLMs favors their embracing as such, it is not clear that they are in a place to offer insights into the target system they seek to represent. After identifying the most important theoretical and empirical risks brought about by the adoption of scientific models that lack transparency, we discuss LLMs relating them to every scientific model's fundamental components: the object, the medium, the meaning and the user. We conclude that, at their current stage of development, LLMs hardly offer any explanations for language, and then we provide an outlook for more informative future research directions on this topic.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 53 references that are in the map. First posted on arXiv 17 Oct 2023.
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12 of 53 references.
References · 12
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- 2021The Values Encoded in Machine Learning Researchcited by 172
- 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
- 2022A very preliminary analysis of DALL-E 2cited by 82
- 2022Meaning without reference in large language modelscited by 76
- 2020Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networkscited by 72
- 2022DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Modelscited by 51
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- 2021Minimum Description Length Recurrent Neural Networkscited by 24
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- 2021Large Language Models are not Models of Natural Language: they are Corpus Modelscited by 6
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