OpenFinLLM Leaderboard Documentation documentation
Introduction
Financial Question Trees
- Overview
- BloombergGPT Datasets
- Information Extraction (IE)
- Textual Analysis (TA)
- Question Answering (QA)
- Text Generation (TG)
- Risk Management (RM)
- Forecasting (FO)
- Decision-Making (DM)
- Other Datasets
Real-world Use Cases
References
Tutorials
- FPB Dataset Evaluation with O3-Mini
- Benchmark DeepSeek on Financial Sentiment Analysis
- Benchmark DeepSeek on Financial Questions with RAG
- Benchmark DeepSeek on Financial Sentiment Analysis (few-shot)
- Benchmark Llama-3.2 on Financial Sentiment Analysis (zeroshot)
- Benchmark GPT-4o on Financial Sentiment Analysis (zeroshot)
- Evaluate Deepseek using Framework
Experiment Settings
Industry Collaboration
- FAQs
- What unique challenges does finance put on the models?
- Can we use a general purpose model or have to use a financially enhanced one?
- What’s the motivation for fintuning a model with financial data?
- When should I choose fine tuning and when should I choose RAG?
- How to get high-quality financial data?
- What is multimodal financial data?
- Should I choose pretraining, fine tuning or calling APIs?
- What’s the potential of reasoning models in finance?
- Contributions