Papers with mathematical question answering
Teaching Small Language Models to Reason for Knowledge-Intensive Multi-Hop Question Answering (2024.findings-acl)
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| Challenge: | Large language models can teach small language models to solve complex reasoning tasks by Chain-of-thought Distillation (CoTD) e.g., mathematical question answering. |
| Approach: | They propose a method that distills two student models to solve a multi-hop question . they use chain-of-thought distillation to generate step-by-step reasoning paths . |
| Outcome: | The proposed method surpasses existing methods on knowledge-intensive multi-hop questions. |
More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are powerful but weak when inputs are perturbed. |
| Approach: | They evaluate LLMs that are more powerful than single LLM in math question answering . they use a unified sampling-and-voting framework to evaluate their models . |
| Outcome: | The proposed models show that collaboration between agents improves accuracy and clean accuracy even with a large number of agents. |