Summary of What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions, by Liyi Zhang et al.
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
by Liyi Zhang, Michael Y. Li, Thomas L. Griffiths
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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 paper explores the properties of autoregressive language model embeddings. It’s been shown that these embeddings capture syntax and semantics, but what exactly should they represent? The authors connect the prediction objective to constructing sufficient statistics for summarizing a sequence of observations. They identify three settings where optimal embedding content can be determined: independent identically distributed data, latent state models, and discrete hypothesis spaces. In each setting, the embedding should reflect the relevant information. To verify this, the authors conduct empirical probing studies using transformers, which demonstrate the ability to encode these types of generating distributions without relying on token memorization. The results also show good performance in out-of-distribution cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about what language models learn from text. Large language models have been shown to capture the meaning and structure of language, but what should they be learning? The authors think that language models should be summarizing information in a sequence of observations. They find three situations where this makes sense: when data comes independently, when there’s a hidden cause for the data, or when we’re trying to figure out which idea is correct. To check their ideas, the authors test how well large language models can summarize information without relying on memorizing specific words. |
Keywords
» Artificial intelligence » Autoregressive » Embedding » Language model » Semantics » Syntax » Token
