Measuring Distributional Shifts in Text: The Advantage of Language
  Model-Based Embeddings
Gyandev Gupta Bashir Rastegarpanah Amalendu Iyer Joshua Rubin Krishnaram Kenthapadi
An essential part of monitoring machine learning models in production is
measuring input and output data drift. In this paper, we present a system for
measuring distributional shifts in natural language data and highlight and
investigate the potential advantage of using large language models (LLMs) for
this problem. Recent advancements in LLMs and their successful adoption in
different domains indicate their effectiveness in capturing semantic
relationships for solving various natural language processing problems. The
power of LLMs comes largely from the encodings (embeddings) generated in the
hidden layers of the corresponding neural network. First we propose a
clustering-based algorithm for measuring distributional shifts in text data by
exploiting such embeddings. Then we study the effectiveness of our approach
when applied to text embeddings generated by both LLMs and classical embedding
algorithms. Our experiments show that general-purpose LLM-based embeddings
provide a high sensitivity to data drift compared to other embedding methods.
We propose drift sensitivity as an important evaluation metric to consider when
comparing language models. Finally, we present insights and lessons learned
from deploying our framework as part of the Fiddler ML Monitoring platform over
a period of 18 months.