Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes (2024.acl-long)
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| Challenge: | Numerical reasoning is an essential ability for NLP systems to handle numeric information. |
| Approach: | They propose a numerical reasoning method that generates reliable reasoning processes by decomposing the answer formula and aim to train models to generate the process with synthesized data. |
| Outcome: | The proposed method improves on all five datasets with an average improvement of 1.8% compared with baselines and gpt-3.5-turbo. |
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| Challenge: | Existing approaches to enhance the reliability of Large Language Models (LLMs) in complex reasoning tasks are limited by their limitations. |
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| Challenge: | Existing methods for constructing process supervision training data are costly or suffer from poor quality. |
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| Challenge: | Empirical results on mathematical reasoning benchmarks substantiate the efficacy of Large language models (LLMs). |
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| Challenge: | Existing numerical reasoning models are too weak for downstream tasks like fact-checking . FERMAT evaluates models on number understanding, mathematical operations, and training dependency . |
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Zhexin Zhang, Xian Qi Loye, Victor Shea-Jay Huang, Junxiao Yang, Qi Zhu, Shiyao Cui, Fei Mi, Lifeng Shang, Yingkang Wang, Hongning Wang, Minlie Huang
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