Summary of Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers, by Kabir Ahuja et al.
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers
by Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Transformers trained on natural language data have been shown to learn its hierarchical structure and generalize to sentences with unseen syntactic structures without explicitly encoding any structural bias. Researchers investigated sources of inductive bias in transformer models and their training that could cause such generalization behavior to emerge. They experimented with transformer models trained on multiple synthetic datasets, using different training objectives, including sequence-to-sequence modeling, prefix language modeling, and found that language modeling consistently led to hierarchical generalization. To study how transformers encode hierarchical structure, they conducted pruning experiments, discovering joint existence of subnetworks within the model with different generalization behaviors (corresponding to hierarchical structure and linear order). Additionally, from a Bayesian perspective, they established a correlation between whether transformers generalize hierarchically on a dataset and whether the simplest explanation of that dataset is provided by a hierarchical grammar compared to regular grammars exhibiting linear generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how transformer models learn and generalize. It shows that these models can learn hierarchical structure from natural language data without any special training. The researchers tested different ways of training transformers on synthetic data and found that the best results came from using the language modeling objective. They also pruned the models to see what happened, which helped them understand how the models were storing their knowledge. Finally, they took a Bayesian approach to understand why transformers prefer hierarchical generalization. |
Keywords
» Artificial intelligence » Generalization » Pruning » Synthetic data » Transformer
