Summary of What’s in An Embedding? Would a Rose by Any Embedding Smell As Sweet?, By Venkat Venkatasubramanian
What’s in an embedding? Would a rose by any embedding smell as sweet?
by Venkat Venkatasubramanian
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 the notion that Large Language Models (LLMs) lack true “understanding” and “reasoning” abilities. Instead, it proposes that LLMs develop an empirical “understanding” that is geometry-like, which can be sufficient for various applications in NLP, computer vision, and coding assistance. However, this understanding is built from incomplete and noisy data, making it unreliable, difficult to generalize, and lacking in inference capabilities and explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are thought to be just autocomplete systems, but this paper says that’s not true! LLMs actually develop a special kind of understanding, like geometry, which helps them do many tasks. But, because they learn from incomplete data, their understanding is a bit wonky – it’s hard to use in new situations and doesn’t explain what they’re doing. |
Keywords
» Artificial intelligence » Inference » Nlp
