Aligning Large and Small Language Models via Chain-of-Thought Reasoning (2024.eacl-long)
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| Challenge: | Chain-of-Thought (CoT) prompting empowers Large Language Models to solve complex reasoning tasks in a step-wise manner. |
| Approach: | They propose a method for aligning and transferring reasoning abilities between larger and smaller Language Models by using CoT-Demonstrations. |
| Outcome: | The proposed method outperforms baselines on question-answering and mathematical reasoning benchmarks. |
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| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
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Xinghao Chen, Zhijing Sun, Guo Wenjin, Miaoran Zhang, Yanjun Chen, Yirong Sun, Hui Su, Yijie Pan, Dietrich Klakow, Wenjie Li, Xiaoyu Shen
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Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning (2025.coling-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. |
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