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Few-shot prompting demonstrates that language models can learn in context even though they were not trained to do. However, explicitly learning to learn in context meta-icl leads to better results. With symbol tuning, labels are replaced with arbitrary symbols e. We implement symbol tuning , as presented in the Symbol tuning improves in-context learning paper with tasksource classification datasets.
An input is a shuffled sequence of 4 positive and 4 negative examples showing a particular label replaced with a symbol - a random word , followed by an example to label. This is the largest symbol-tuning dataset to date, with datasets. Symbol tuning improves in-context learning, which tends to be degraded by instruction tuning. This is well enough to challenge long range modeling. But be careful to remove examples that are too long or to truncate from left, otherwise some examples might be unsolvable, as the "question" are at the end of the examples.
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