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Question Answering On Finqa
Metrics
Execution Accuracy
Program Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | |||
|---|---|---|---|---|
| APOLLO | 71.07 | 68.94 | APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning | |
| ELASTIC (RoBERTa-large) | 68.96 | 65.21 | ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler | |
| GPT-4 (8k) | 68.79 | - | Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks | - |
| FinQANet (RoBERTa-large) | 65.05 | 63.52 | FinQA: A Dataset of Numerical Reasoning over Financial Data | |
| FinQANet (BERT-large) | 57.43 | 55.52 | FinQA: A Dataset of Numerical Reasoning over Financial Data | |
| FinQANet (FinBert ) | 53.71 | 51.71 | FinQA: A Dataset of Numerical Reasoning over Financial Data |
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