Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance
Autor: | Gandouet, Olivier, Belbahri, Mouloud, Jezequel, Armelle, Bodjov, Yuriy |
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Rok vydání: | 2024 |
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Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Edge Intelligence Workshop at AAAI24 |
Databáze: | arXiv |
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