Summary of Reinterpreting ‘the Company a Word Keeps’: Towards Explainable and Ontologically Grounded Language Models, by Walid S. Saba
Reinterpreting ‘the Company a Word Keeps’: Towards Explainable and Ontologically Grounded Language Models
by Walid S. Saba
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper argues that the success of large language models (LLMs) is not due to their symbolic or subsymbolic nature, but rather because they employ a successful bottom-up strategy of reverse engineering language at scale. However, LLMs’ knowledge about language is buried in millions of weights and therefore unexplainable. Additionally, LLMs often fail to make correct inferences in linguistic contexts requiring reasoning. To address these limitations, the paper suggests employing a symbolic setting using the same bottom-up strategy, resulting in explainable, language-agnostic, and ontologically grounded language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are very good at processing natural language, but experts think it’s because of how they’re trained, not because of what kind of math they use. This makes their knowledge hard to understand or explain. Also, LLMs often make mistakes when trying to figure out the meaning of sentences that involve things like time, space, or possibility. To fix these problems, researchers suggest training language models in a way that’s more like how humans think and reason. |
