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cs.CL2022★ 45 cited
Efficient Training of Language Models to Fill in the Middle
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak +4
We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the midd…
cs.CL2022★ 152 cited
Text and Code Embeddings by Contrastive Pre-Training
Arvind Neelakantan, Tao Xu, Raul Puri +22
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use c…