Understanding In-Context Learning with a Pelican Soup Framework
This work addresses theoretical limitations in understanding in-context learning for NLP researchers, though it appears incremental by building on existing frameworks.
The authors tackled the gap between theoretical analyses and practical in-context learning in NLP by proposing the Pelican Soup Framework, which establishes a loss bound of O(1/T) and explains generalization to unseen tasks, with empirical validation on GPT2-Large showing efficacy.
Many existing theoretical analyses of in-context learning for natural language processing are based on latent variable models that leaves gaps between theory and practice. We aim to close these gaps by proposing a theoretical framework, the Pelican Soup Framework. In this framework, we introduce (1) the notion of a common sense knowledge base, (2) a general formalism for natural language classification tasks, and the notion of (3) meaning association. Under this framework, we can establish a $\mathcal{O}(1/T)$ loss bound for in-context learning, where $T$ is the number of example-label pairs in the demonstration. Compared with previous works, our bound reflects the effect of the choice of verbalizers and the effect of instruction tuning. An additional notion of \textit{atom concepts} makes our framework possible to explain the generalization to tasks unseen in the language model training data. Finally, we propose a toy setup, Calcutec, and a digit addition task that mimics types of distribution shifts a model needs to overcome to perform in-context learning. We also experiment with GPT2-Large on real-world NLP tasks. Our empirical results demonstrate the efficacy of our framework to explain in-context learning.