The unprecedented success of LLMs in carrying out linguistic interactions disguises the fact that, on closer inspection, their knowledge of meaning and their inference abilities are still quite limited and different from human ones. They generate human-like texts, but still fall short of fully understanding them. I will refer to this as the “semantic gap” of LLMs. Some claim that this gap depends on the lack of grounding of text-only LLMs. I instead argue that the problem lies in the very type of representations these models acquire. They learn highly complex association spaces that correspond only partially to truly semantic and inferential ones. This prompts the need to investigate the missing links to bridge the gap between LLMs as sophisticated statistical engines and full-fledged semantic agents.

 

References

Lenci A., & Sahlgren (2023). Distributional Semantics, Cambridge, Cambridge University Press.

Lenci, A. (2023). Understanding Natural Language Understanding Systems. A Critical Analysis. Sistemi Intelligenti, arXiv preprint arXiv:2303.04229.

Lenci, A., & Padó, S. (2022). Perspectives for natural language processing between AI, linguistics and cognitive science. Frontiers in Artificial Intelligence, 5, 1059998.

Lenci, A. (2018). Distributional models of word meaning. Annual review of Linguistics, 4, 151-171.