Summary of Arrows Of Time For Large Language Models, by Vassilis Papadopoulos et al.
Arrows of Time for Large Language Models
by Vassilis Papadopoulos, Jérémie Wenger, Clément Hongler
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 explores the probabilistic modeling capabilities of Autoregressive Large Language Models (LLMs) in terms of time directionality. Building upon Shannon’s 1951 work, the study finds that larger models exhibit a subtle yet consistent asymmetry in their ability to predict natural language tokens – with more accuracy when predicting future tokens than past ones. This phenomenon is theoretically unexpected from an information-theoretic perspective and can be attributed to sparsity and computational complexity considerations. The paper provides a theoretical framework to explain this asymmetry and opens up new perspectives for further investigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how big language models, like the ones used in text processing, make predictions about what comes next in a sentence or text. Surprisingly, these models are better at predicting what comes next than what came before! This is unusual because you might think it would be just as easy to predict either way. The researchers found that this difference happens even when they use different types of language and bigger models. They came up with an explanation for why this might happen and think it could lead to new ways of understanding how these language models work. |
Keywords
* Artificial intelligence * Autoregressive
