A Scalable and Adaptive System to Infer the Industry Sectors of
  Companies: Prompt + Model Tuning of Generative Language Models
Lele Cao Vilhelm von Ehrenheim Astrid Berghult Cecilia Henje Richard Anselmo Stahl Joar Wandborg Sebastian Stan Armin Catovic Erik Ferm Hannes Ingelhag
The Private Equity (PE) firms operate investment funds by acquiring and
managing companies to achieve a high return upon selling. Many PE funds are
thematic, meaning investment professionals aim to identify trends by covering
as many industry sectors as possible, and picking promising companies within
these sectors. So, inferring sectors for companies is critical to the success
of thematic PE funds. In this work, we standardize the sector framework and
discuss the typical challenges; we then introduce our sector inference system
addressing these challenges. Specifically, our system is built on a
medium-sized generative language model, finetuned with a prompt + model tuning
procedure. The deployed model demonstrates a superior performance than the
common baselines. The system has been serving many PE professionals for over a
year, showing great scalability to data volume and adaptability to any change
in sector framework and/or annotation.