  In-context learning (ICL) is an important paradigm for adapting large
language models (LLMs) to new tasks, but the generalization behavior of ICL
remains poorly understood. We investigate the inductive biases of ICL from the
perspective of feature bias: which feature ICL is more likely to use given a
set of underspecified demonstrations in which two features are equally
predictive of the labels. First, we characterize the feature biases of GPT-3
models by constructing underspecified demonstrations from a range of NLP
datasets and feature combinations. We find that LLMs exhibit clear feature
biases - for example, demonstrating a strong bias to predict labels according
to sentiment rather than shallow lexical features, like punctuation. Second, we
evaluate the effect of different interventions that are designed to impose an
inductive bias in favor of a particular feature, such as adding a natural
language instruction or using semantically relevant label words. We find that,
while many interventions can influence the learner to prefer a particular
feature, it can be difficult to overcome strong prior biases. Overall, our
results provide a broader picture of the types of features that ICL may be more
likely to exploit and how to impose inductive biases that are better aligned
with the intended task.
