Summary of Enhancing Tinybert For Financial Sentiment Analysis Using Gpt-augmented Finbert Distillation, by Graison Jos Thomas
Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation
by Graison Jos Thomas
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed study enhances the performance of BERT-based models for financial sentiment analysis by leveraging large language models (LLMs) like GPT-4 Omni. The researchers develop synthetic training data using LLMs to address data scarcity challenges, improving the accuracy of smaller models and making them competitive with larger counterparts. They fine-tune FinBERT, a BERT model for financial sentiment analysis, and create TinyFinBERT, a compact transformer model, through knowledge distillation. The study demonstrates the effectiveness of innovative data augmentation and distillation techniques in advancing financial sentiment analysis using LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve financial sentiment analysis by making smaller models more efficient and accurate. It uses large language models to generate new training examples and transform existing data, which enhances the performance of FinBERT and prepares it to serve as a teacher model. The study shows how LLMs can contribute to advancements in financial sentiment analysis by improving smaller models through innovative techniques. |
Keywords
» Artificial intelligence » Bert » Data augmentation » Distillation » Gpt » Knowledge distillation » Teacher model » Transformer
