Summary of Large Language Models and Linguistic Intentionality, by Jumbly Grindrod
Large language models and linguistic intentionality
by Jumbly Grindrod
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper questions whether large language models like Chat-GPT or LLaMa truly understand the meaning behind the words they generate. Some initial studies have shown that these models meet certain criteria for entering meaningful states, but this paper takes a different approach by considering whether language models meet criteria for linguistic content according to metasemantic theories. The author applies two such theories – Gareth Evans’ account of naming practices and Ruth Millikan’s teleosemantics – to the case of language models, arguing that their outputs are indeed meaningful despite not meeting conditions for mental intentionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores whether large language models like Chat-GPT or LLaMa truly understand what they’re saying. Some studies have shown that these models can behave in ways that seem intelligent, but is it just clever prediction or do they really get the meaning? The researchers take a different approach by looking at how well these models match up to theories about how language works. They apply two specific ideas from famous linguists to see if their predictions make sense. |
Keywords
» Artificial intelligence » Gpt » Llama
