Re-examining learning linear functions in context
Keywords: In context learning, GPT, limitations
TL;DR: We point out several systematic failures and limitations of In Context Learning.
Abstract: In context learning (ICL) is an attractive method of solving a wide range of problems. Inspired by Garg et al., we look closely at ICL in a variety of train and test settings for several transformer models of different sizes trained from scratch. Our study complements prior work by pointing out several systematic failures of these models to generalize to data not in the training distribution, thereby showing some limitations of ICL. We find that models adopt a strategy for this task that is very different from standard solutions.
Supplementary Material: zip
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 14174
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