Papers › APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
Jiashuo Sun, Hang Zhang, Chen Lin, Xiangdong Su, Yeyun Gong, Jian Guo
Long-form numerical reasoning in financial analysis aims to generate a reasoning program to calculate the correct answer for a given question. Previous work followed a retriever-generator framework, where the retriever selects key facts from a long-form document, and the generator generates a reasoning program based on retrieved facts. However, they treated all facts equally without considering the different contributions of facts with and without numbers. Meanwhile, the program consistency were ignored under supervised training, resulting in lower training accuracy and diversity. To solve these problems, we proposed APOLLO to improve the long-form numerical reasoning framework. For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts. For the generator, we design consistency-based reinforcement learning and target program augmentation strategy based on the consistency of program execution results. Experimental results on the FinQA and ConvFinQA leaderboard verify the effectiveness of our proposed method, achieving the new state-of-the-art.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conversational Question Answering | ConvFinQA | APOLLO | Execution Accuracy | 78.76 | #1 of 2 | Archive leaderboard | report |
| Conversational Question Answering | ConvFinQA | APOLLO | Program Accuracy | 77.19 | #1 of 2 | Archive leaderboard | report |
| Question Answering | FinQA | APOLLO | Execution Accuracy | 71.07 | #1 of 6 | Archive leaderboard | report |
| Question Answering | FinQA | APOLLO | Program Accuracy | 68.94 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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