Semiparametric Token-Sequence Co-Supervision
Hyunji Lee Doyoung Kim Jihoon Jun Sejune Joo Joel Jang Kyoung-Woon On Minjoon Seo
In this work, we introduce a semiparametric token-sequence co-supervision
training method. It trains a language model by simultaneously leveraging
supervision from the traditional next token prediction loss which is calculated
over the parametric token embedding space and the next sequence prediction loss
which is calculated over the nonparametric sequence embedding space. The
nonparametric sequence embedding space is constructed by a separate language
model tasked to condense an input text into a single representative embedding.
Our experiments demonstrate that a model trained via both supervisions
consistently surpasses models trained via each supervision independently.
Analysis suggests that this co-supervision encourages a broader generalization
capability across the model. Especially, the robustness of parametric token
space which is established during the pretraining step tends to effectively
enhance the stability of nonparametric sequence embedding space, a new space
established by another language model.