# PROPS: Probabilistic personalization of black-box sequence models

## TL;DR

PROPS is a probabilistic transfer learning method that fine-tunes black-box sequence models for personalized applications, demonstrated on customizing language models for Trump's tweets with minimal additional data.

## Contribution

The paper introduces PROPS, a novel probabilistic perturbation approach for transfer learning in sequential models, enabling lightweight personalization of complex black-box models.

## Key findings

- Effective personalization of language models with only 2,000 words.
- PROPS provides probabilistic insights into model deviations from generic patterns.
- Demonstrated on Wikipedia and Twitter data.

## Abstract

We present PROPS, a lightweight transfer learning mechanism for sequential data. PROPS learns probabilistic perturbations around the predictions of one or more arbitrarily complex, pre-trained black box models (such as recurrent neural networks). The technique pins the black-box prediction functions to "source nodes" of a hidden Markov model (HMM), and uses the remaining nodes as "perturbation nodes" for learning customized perturbations around those predictions. In this paper, we describe the PROPS model, provide an algorithm for online learning of its parameters, and demonstrate the consistency of this estimation. We also explore the utility of PROPS in the context of personalized language modeling. In particular, we construct a baseline language model by training a LSTM on the entire Wikipedia corpus of 2.5 million articles (around 6.6 billion words), and then use PROPS to provide lightweight customization into a personalized language model of President Donald J. Trump's tweeting. We achieved good customization after only 2,000 additional words, and find that the PROPS model, being fully probabilistic, provides insight into when President Trump's speech departs from generic patterns in the Wikipedia corpus. Python code (for both the PROPS training algorithm as well as experiment reproducibility) is available at https://github.com/cylance/perturbed-sequence-model.