Acquiring Bidirectionality via Large and Small Language Models

Abstract

Using token representation from bidirectional language models (LMs) such as BERT is still a widely used approach for token-classification tasks. Even though there exist much larger unidirectional LMs such as Llama-2, they are rarely used to replace the token representation of bidirectional LMs. In this work, we hypothesize that their lack of bidirectionality is what is keeping unidirectional LMs behind. To that end, we propose to newly train a small backward LM and concatenate its representations to those of an existing LM for downstream tasks. Through experiments in token-classification tasks, we demonstrate that introducing backward model can improve the benchmark performance by more than 10 points. Furthermore, we show that the proposed method is especially effective for rare domains and in few-shot learning settings.