| Challenge: | a mathematical word problem requires a method that articulates the problem, the lens through which models view human lexical expressions. |
| Approach: | They propose a method to mimic human thought expansion mechanisms to generate thought expansions . they show ATHENA can be used to generate reasonable thoughts by selecting valid pathways . |
| Outcome: | The proposed model is compelling in variant questions even when the informativeness in training examples is restricted. |
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| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
| Approach: | They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges . |
| Outcome: | The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems. |
Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)
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Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang
| Challenge: | Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans. |
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A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
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| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
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Learn Beyond The Answer: Training Language Models with Reflection for Mathematical Reasoning (2024.emnlp-main)
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| Challenge: | Existing studies focus on *broadening* the training set with data augmentation techniques to maximize such benefits. |
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Learning Multi-Step Reasoning by Solving Arithmetic Tasks (2023.acl-short)
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| Challenge: | Recent studies have demonstrated large LMs’ impressive performance in solving math problems, but such ability seems only to emerge from models with abundant parameters. |
| Approach: | They propose to continuously pre-train LMs with the capabilities of multi-step reasoning by continuously pretraining them on a synthetic dataset MsAT. |
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NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks (2022.acl-long)
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
| Challenge: | Existing AI systems fail to perform basic mathematical reasoning when presented in a slightly different manner. |
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Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)
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| Challenge: | Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning. |
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Self-training Language Models for Arithmetic Reasoning (2024.findings-emnlp)
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| Challenge: | Recent work improves the reasoning capabilities of language models by scaling training data to more diverse or complex collections, but reaching further improvements becomes exceedingly expensive. |
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MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
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| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
| Approach: | They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver . |
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Chaining Simultaneous Thoughts for Numerical Reasoning (2022.findings-emnlp)
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| Challenge: | Numerical reasoning over text is an essential skill for AI systems . structure modeling is effective, but structures restrict how a model should grasp the reasoning process . |
| Approach: | They propose a numerical reasoner that models reasoning steps using a directed acyclic graph without pre-defined decoding dependencies. |
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