Papers by Xin Men
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2025.findings-acl)
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Xin Men, Mingyu Xu, Qingyu Zhang, Qianhao Yuan, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, Weipeng Chen
| Challenge: | Recent studies have identified significant redundancy in large language models . quantization and pruning are two methods that reduce computational resources . |
| Approach: | They propose simple pruning methods that prune redundant layers based on their BI scores. |
| Outcome: | The proposed pruning methods demonstrate superior performance over previous pruning methods. |
Socratic Style Chain-of-Thoughts Help LLMs to be a Better Reasoner (2025.findings-acl)
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| Challenge: | Synthetic data generation is a promising approach to enhance reasoning capabilities of large language models. |
| Approach: | They propose a multi-agent debate framework based on the Socratic questioning strategy . they use socratic questions to deepen the thinking process and broaden it to motivate self-reflection . |
| Outcome: | The proposed framework outperforms existing methods on math and code generation tasks while maintaining affordable costs. |