Summary of Symbolic and Language Agnostic Large Language Models, by Walid S. Saba
Symbolic and Language Agnostic Large Language Models
by Walid S. Saba
First submitted to arxiv on: 27 Aug 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 challenges the notion that the success of large language models (LLMs) is due to the symbolic vs. subsymbolic debate. Instead, it proposes that LLMs’ success stems from their ability to employ a bottom-up strategy for reverse-engineering language at scale. However, this approach also means that the knowledge acquired by these systems about language is buried in millions of microfeatures (weights) that are not meaningful on their own due to their subsymbolic nature. Furthermore, LLMs’ stochastic nature can lead to failures in capturing inferential aspects prevalent in natural language. To overcome these limitations, the authors suggest applying the successful bottom-up strategy in a symbolic setting, creating symbolic, language-agnostic, and ontologically grounded large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models are really good at understanding language. But instead of saying that’s because they’re using symbols or not, it says it’s because they’re using a special way of learning called bottom-up. This means they learn by looking at tiny pieces of language and putting them together to understand bigger things. The problem is that this approach hides the knowledge it gains about language in lots of small details that aren’t useful on their own. Also, these models can make mistakes when trying to figure out how people use language to make inferences. To fix this, the researchers suggest using a similar way of learning but with symbols instead of tiny pieces of language. |
