Papers with math problem
Unleashing the Reasoning Potential of LLMs by Critique Fine-Tuning on One Problem (2025.emnlp-main)
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| Challenge: | Critique Fine-Tuning (CFT) is a promising paradigm for unlocking the reasoning capabilities of large language models. |
| Approach: | They propose a method that leverages critique data generated from a single math problem to improve reasoning accuracy. |
| Outcome: | The proposed method surpasses one-shot RLVR while requiring 15 to 20 times less compute. |
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 . |
| Outcome: | The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset. |
From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? (2025.emnlp-main)
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| Challenge: | Existing studies that create problem variants by adding perturbations to a single problem focus on the interaction between problems. |
| Approach: | They propose a pipeline with 98.2% accuracy to combine two original problems with a logical connection and to evaluate LLMs' generalization ability on the compositional problems. |
| Outcome: | The proposed pipeline can combine two original problems with a logical connection to get a new math problem and evaluate its compositional generalization on the compositional problems. |
Empowering Math Problem Generation and Reasoning for Large Language Model via Synthetic Data based Continual Learning Framework (2025.emnlp-main)
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| Challenge: | Existing learning frameworks for large language models (LLMs) for math problem generation are limited and lack quality data. |
| Approach: | They propose a synthetic data based continual learning framework to improve LLMs ability for MPG and math reasoning. |
| Outcome: | The proposed framework improves performance on large language models and math reasoning using supervised fine-tuning, data synthesis and direct preference optimization. |