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"FANAL -- Financial Activity News Alerting Language Modeling Framework"

The podcast on this paper is generated with Google's Illuminate.

FANAL slashes financial news processing costs to 0.001% of GPT-4 while maintaining superior accuracy

FANAL is a specialized BERT-based framework for real-time financial event detection that categorizes news into 12 distinct categories. It uses silver-labeled data through XGBoost and employs ORPO fine-tuning, creating ORBERT for superior class-wise probability calibration, outperforming GPT-4 and other LLMs at fraction of the cost.

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https://arxiv.org/abs/2412.03527

🔍 Original Problem:

→ Financial markets generate massive news streams that overwhelm traditional analysis methods

→ Existing LLMs are computationally expensive and impractical for real-time financial news processing

→ Current solutions lack granular categorization needed for precise decision-making

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💡 Solution in this Paper:

→ FANAL processes financial news through a BERT model fine-tuned with ORPO technique

→ It uses XGBoost for creating silver-labeled training data from just 1,200 manually labeled samples

→ The framework introduces ORBERT, optimizing class-wise probability calibration

→ Entity Relevance Module enhances news interpretation for specific financial signals

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🎯 Key Insights:

→ Silver labeling with minimal manual data can achieve competitive performance

→ ORPO fine-tuning significantly improves class balance handling

→ Smaller, specialized models can outperform larger LLMs in specific domains

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📊 Results:

→ Processes 10,000 articles in 0.013 hours vs GPT-4's 1.579 hours

→ Costs $0.0017 for 10,000 articles vs GPT-4's $204.87

→ Achieves 96%+ accuracy in categories like Bankruptcy and Strategic Alliances

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