An Ensemble Approach to Personalized Real Time Predictive Writing for
  Experts
Sourav Prosad Viswa Datha Polavarapu Shrutendra Harsola
Completing a sentence, phrase or word after typing few words / characters is
very helpful for Intuit financial experts, while taking notes or having a live
chat with users, since they need to write complex financial concepts more
efficiently and accurately many times in a day. In this paper, we tie together
different approaches like large language models, traditional Markov Models and
char level models to create an end-to-end system to provide personalised
sentence/word auto-complete suggestions to experts, under strict latency
constraints. Proposed system can auto-complete sentences, phrases or words
while writing with personalisation and can be trained with very less data and
resources with good efficiency. Our proposed system is not only efficient and
personalized but also robust as it leverages multiple machine learning
techniques along with transfer learning approach to fine tune large language
model with Intuit specific data. This ensures that even in cases of rare or
unusual phrases, the system can provide relevant auto-complete suggestions in
near real time. Survey has showed that this system saves expert note-taking
time and boosts expert confidence in their communication with teammates and
clients. Since enabling this predictive writing feature for QBLive experts,
more than a million keystrokes have been saved based on these suggestions. We
have done comparative study for our ensemble choice. Moreover this feature can
be integrated with any product which has writing facility within a very short
period of time.