Summary of Fanal — Financial Activity News Alerting Language Modeling Framework, by Urjitkumar Patel et al.
FANAL – Financial Activity News Alerting Language Modeling Framework
by Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar, Hari Nalluri
First submitted to arxiv on: 4 Dec 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 A novel BERT-based framework called FANAL (Financial Activity News Alerting Language Modeling Framework) is introduced to accurately detect and analyze real-time financial events. By leveraging silver-labeled data processed through XGBoost and employing advanced fine-tuning techniques, FANAL categorizes news into twelve distinct financial categories. Additionally, the paper presents ORBERT (Odds Ratio BERT), a variant of BERT fine-tuned with ORPO (Odds Ratio Preference Optimization) for superior class-wise probability calibration. The performance of FANAL is evaluated against leading large language models such as GPT-4o, Llama-3.1 8B, and Phi-3, demonstrating its superior accuracy and cost efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FANAL is a new way to quickly understand financial news and events. It’s like having a super-smart assistant that can help you make sense of the latest market trends. By using special techniques and data, FANAL can identify important financial information and categorize it into different areas. This makes it easier for people in finance to stay up-to-date and make informed decisions. |
Keywords
» Artificial intelligence » Bert » Fine tuning » Gpt » Llama » Optimization » Probability » Xgboost
