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English(EN) InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation

新系统InFerActive提高了LLM安全评估的效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了InFerActive，一个旨在提高大型语言模型安全评估效率的交互式系统。该系统将LLM采样结果可视化为一棵可导航的树，使评估人员能够高效地探索和过滤潜在的有害响应。用户研究表明，与传统的电子表格方法相比，InFerActive显著提高了评估效率和覆盖范围，所需的样本数量减少了多达五倍。 AI

影响 提高了LLM安全评估的效率，有望带来更强大、更安全的AI部署。

排序理由 该集群包含一篇详细介绍LLM安全评估新系统的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新系统InFerActive提高了LLM安全评估的效率

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该集群包含一篇详细介绍LLM安全评估新系统的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
paper, safety
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92 days old
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junhyeong Hwangbo, Soohyun Lee, Hyeon Jeon, Kyochul Jang, Minsoo Cheong, Youngjae Yu, Jinwook Seo ·

    InFerActive：用于LLM采样安全评估的交互式树状探索

    arXiv:2512.10234v2 Announce Type: replace-cross Abstract: Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different responses to the same prompt, low-probability harmful outputs can still reach use…

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