Papers with GSM-Symbolic
ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to train large language models do not capture how humans learn to think. |
| Approach: | They propose a method to fine-tune large language models for mathematical reasoning by using a text-infilling task that predicts masked equations from a given solution. |
| Outcome: | Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses baseline Masked Thought in performance and robustness with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. |
Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions (2025.acl-long)
Copied to clipboard
| Challenge: | Recent work suggests using logical formalisms coupled with external symbolic solvers to solve complex tasks. |
| Approach: | They propose a framework to disentangle content from logical reasoning without a complete formalisation. |
| Outcome: | The proposed methods improve CoT-based methods by up to 8% accuracy on challenging adversarial variations on both natural language and symbolic reasoning tasks. |