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English(EN) SafeMath: Inference-time Safety improves Math Accuracy

新技术SafeMath在提高LLM数学准确性的同时增强了安全性

研究人员推出了一种新颖的安全对齐技术SafeMath,旨在减轻大型语言模型(LLM)在处理数学问题时产生有害输出的问题。该技术旨在解决LLM通过嵌入偏见或不道德内容的对抗性输入而被操纵的问题,尤其是在教育环境中。为了促进这项研究,研究团队还开发了ToxicGSM,这是一个包含1.9k个算术问题和嵌入式敏感上下文的数据集,用于审计现有LLM并分析安全性和准确性之间的权衡。SafeMath不仅减少了有害输出,还保持甚至提高了数学推理能力,表明安全性和准确性并非相互排斥。 AI

影响 增强了LLM在数学任务中的安全性,可能减少有害内容在教育环境中的传播。

排序理由 该集群包含一篇研究论文,详细介绍了用于LLM在数学环境中安全性的新技术和数据集。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新技术SafeMath在提高LLM数学准确性的同时增强了安全性

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该集群包含一篇研究论文,详细介绍了用于LLM在数学环境中安全性的新技术和数据集。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sagnik Basu, Subhrajit Mitra, Aman Juneja, Somnath Banerjee, Rima Hazra, Animesh Mukherjee ·

    SafeMath:推理时安全性能提升数学准确性

    arXiv:2603.25201v2 Announce Type: replace Abstract: Recent research points toward LLMs being manipulated through adversarial and seemingly benign inputs, resulting in harmful, biased, or policy-violating outputs. In this paper, we study an underexplored issue concerning harmful a…