Spactor-t5: Pre-training T5 Models With Span Corruption And Replaced Token Detection
Ye Ke, Jiang Heinrich, Rostamizadeh Afshin, Chakrabarti Ayan, Desalvo Giulia, Kagy Jean-françois, Karydas Lazaros, Citovsky Gui, Kumar Sanjiv. Arxiv 2024
[Paper]
Pre-training large language models is known to be extremely resource intensive and often times inefficient, under-utilizing the information encapsulated in the training text sequences. In this paper, we present SpacTor, a new training procedure consisting of (1) a hybrid objective combining span corruption (SC) and token replacement detection (RTD), and (2) a two-stage curriculum that optimizes the hybrid objective over the initial \(\tau\) iterations, then transitions to standard SC loss. We show empirically that the effectiveness of the hybrid objective is tied to the two-stage pre-training schedule, and provide extensive analysis on why this is the case. In our experiments with encoder-decoder architectures (T5) on a variety of NLP tasks, SpacTor-T5 yields the same downstream performance as standard SC pre-training, while enabling a 50% reduction in pre-training iterations and 40% reduction in total FLOPs. Alternatively, given the same amount of computing budget, we find that SpacTor results in significantly improved downstream benchmark performance.
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