Free Lunch for Efficient Textual Commonsense Integration in Language
  Models
Wanyun Cui Xingran Chen
Recent years have witnessed the emergence of textual commonsense knowledge
bases, aimed at providing more nuanced and context-rich knowledge. The
integration of external commonsense into language models has been shown to be a
key enabler in advancing the state-of-the-art for a wide range of NLP tasks.
However, incorporating textual commonsense descriptions is computationally
expensive, as compared to encoding conventional symbolic knowledge. In this
paper, we propose a method to improve its efficiency without modifying the
model. We group training samples with similar commonsense descriptions into a
single batch, thus reusing the encoded description across multiple samples. One
key observation is that the upper bound of batch partitioning can be reduced to
the classic {\it graph k-cut problem}. Consequently, we propose a spectral
clustering-based algorithm to solve this problem. Extensive experiments
illustrate that the proposed batch partitioning approach effectively reduces
the computational cost while preserving performance. The efficiency improvement
is more pronounced on larger datasets and on devices with more memory capacity,
attesting to its practical utility for large-scale applications.