Out-of-distribution generalization via composition: A lens through induction heads in Transformers
View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) achieve out-of-distribution (OOD) generalization by composing self-attention layers. A shared latent subspace, the common bridge representation, enables this rule inference and composition for novel tasks.
Area Of Science
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
Background
- Large language models (LLMs) exhibit emergent abilities, including solving novel tasks with few examples.
- Out-of-distribution (OOD) generalization, the ability to perform on unseen data distributions, is crucial for LLM capabilities.
- The mechanisms behind LLM OOD generalization, particularly in rule-inference tasks, are not well understood.
Purpose Of The Study
- To investigate how large language models achieve out-of-distribution generalization.
- To explore the role of hidden rule inference and composition in LLM performance on novel tasks.
- To examine the internal dynamics of Transformer models, specifically induction heads, during OOD generalization.
Main Methods
- Empirical examination of Transformer training dynamics on synthetic data.
- Extensive experiments on various pre-trained LLMs, focusing on self-attention mechanisms.
- Analysis of rule inference in in-context learning settings with symbolic reasoning.
Main Results
- Out-of-distribution generalization is intrinsically linked to compositional abilities in LLMs.
- Models can learn hidden rules by composing two self-attention layers, leading to OOD generalization.
- A shared latent subspace, termed the common bridge representation, facilitates composition by aligning early and later layers.
Conclusions
- LLMs achieve OOD generalization through the composition of self-attention layers.
- The common bridge representation hypothesis explains how latent feature alignment enables rule composition.
- Understanding these mechanisms is key to unlocking more robust and generalizable AI systems.
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