# Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance

arXiv: https://arxiv.org/abs/2403.02185 · HTML: https://mlsanity.com/paper/2403.02185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [FinBERT: A Pretrained Language Model for Financial Communications](https://mlsanity.com/paper/2006.08097/) (2020)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)

## Built on by (most cited first, 1 of 1)

- [Agentic Retrieval of Topics and Insights from Earnings Calls](https://mlsanity.com/paper/2507.07906/) (2025)

## Abstract (arXiv)

In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnings calls. We detail a training approach that merges knowledge distillation and transfer learning, resulting in lightweight topic and sentiment classification models without significant loss in accuracy. These models are assessed through a dataset annotated by experts. The paper also delves into two practical case studies, highlighting how the generated features can be effectively utilized in quantitative investing scenarios.