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ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data. (arXiv:2205.12600v1 [cs.CL])
May 26, 2022, 1:12 a.m. | Xiaochuang Han, Yulia Tsvetkov
cs.CL updates on arXiv.org arxiv.org
Large pretrained language models have been performing increasingly well in a
variety of downstream tasks via prompting. However, it remains unclear from
where the model learns the task-specific knowledge, especially in a zero-shot
setup. In this work, we want to find evidence of the model's task-specific
competence from pretraining and are specifically interested in locating a very
small subset of pretraining data that directly supports the model in the task.
We call such a subset supporting data evidence and propose …
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