Summary of How Transformers Learn Structured Data: Insights From Hierarchical Filtering, by Jerome Garnier-brun et al.
How transformers learn structured data: insights from hierarchical filtering
by Jerome Garnier-Brun, Marc Mézard, Emanuele Moscato, Luca Saglietti
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); 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 Understanding the learning process in transformers is crucial for developing interpretable AI. Our study introduces a hierarchical filtering procedure for generative models on trees, enabling us to control positional correlations. We show that vanilla transformer encoders can approximate exact inference algorithms when trained on root classification and masked language modeling tasks. Analysis reveals that correlations at larger distances are sequentially included during training, mirroring the hierarchy’s levels. Furthermore, attention maps from models with varying filtering and probing different encoder levels demonstrate a reconstruction of correlations across length scales, corresponding to the hierarchy’s levels. We relate this to a plausible implementation of exact inference within the same architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to understand how AI machines learn and make decisions. They developed a new way to control how AI models process information. This helps us figure out why AI makes certain choices. The study shows that some types of AI can do the same calculations as humans, but in a different way. When training these AI models, they start with small pieces of information and build up to larger patterns. By looking at what happens during this process, we can understand how AI is “thinking” and make it more transparent. |
Keywords
» Artificial intelligence » Attention » Classification » Encoder » Inference » Transformer
