From Implicit Exploration to Structured Reasoning: Guideline and Refinement for LLMs (2025.findings-emnlp)
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| Challenge: | Existing models rely on implicit exploration, which leads to unstable reasoning paths and lack of error correction. |
| Approach: | They propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. |
| Outcome: | The proposed model outperforms strong baselines on the Big-Bench Hard benchmark. |
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
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| Challenge: | Large language models have demonstrated strong performance in a wide-range of language tasks without task-specific fine-tuning. |
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
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| Challenge: | Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation. |
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
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| Challenge: | Recent advances in large language models have improved multistep reasoning but they lose focus over the middle of long contexts. |
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Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
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