Summary of Distilled Chatgpt Topic & Sentiment Modeling with Applications in Finance, by Olivier Gandouet et al.
Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance
by Olivier Gandouet, Mouloud Belbahri, Armelle Jezequel, Yuriy Bodjov
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study leverages ChatGPT to develop simplified models that produce easily interpretable features. These features are used to analyze financial outcomes from earnings calls. A training approach combining knowledge distillation and transfer learning is employed, yielding lightweight topic and sentiment classification models without sacrificing accuracy. The models are evaluated using a dataset annotated by experts. Two practical case studies demonstrate the effectiveness of the generated features in quantitative investing scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to create simple models that can understand and analyze financial data from company earnings calls. These models are trained using two techniques: knowledge distillation, which helps them learn from other models, and transfer learning, which allows them to adapt to new tasks. The models are tested on a dataset checked by experts and show promise for use in making investment decisions. |
Keywords
* Artificial intelligence * Classification * Knowledge distillation * Transfer learning
