A Meta-Learning Perspective on Transformers for Causal Language Modeling
This work provides incremental insights into understanding Transformer mechanisms for researchers in natural language processing.
The authors tackled the problem of explaining the Transformer's capabilities in causal language modeling by establishing a meta-learning perspective and analyzing token representation norms, with theoretical and experimental support.
The Transformer architecture has become prominent in developing large causal language models. However, mechanisms to explain its capabilities are not well understood. Focused on the training process, here we establish a meta-learning view of the Transformer architecture when trained for the causal language modeling task, by explicating an inner optimization process within the Transformer. Further, within the inner optimization, we discover and theoretically analyze a special characteristic of the norms of learned token representations within Transformer-based causal language models. Our analysis is supported by experiments in various settings.