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Oct 12 · Monday

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Found 4 papers
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Model risks

Misalignment and value divergence4Inappropriate refusal30Bias and discrimination4

Impact

Harmful content4Output manipulation4Availability and reliability3

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Models and APIs4

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Model layer4
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Model risks：Bias and discriminationImpact：Harmful contentLayer：Model layerClear all

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  1. Analysis and theoryarXiv 2609.20779

    研究称 GPT 系列安全训练把性别歧视转化而非消除

    提出 harm laundering，证明安全训练改写而非消除性别歧视

    arXiv
    2609.20779
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    gpt2, gpt2-medium, gpt2-large and others15
    AbstractGPT 谱系上毒性下降与表征伤害上升并存。

    Wyer、Black 与 Al Moubayed 用 OpenAI GPT 谱系检验：毒性分数下降是否等于歧视减少。 他们的答案是否定的，并把“显性伤害被改写成评测指标看不见的形式”称为 harm laundering。

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  2. Evaluation and benchmarksarXiv 2603.08275

    AdaCultureSafe：LLM 文化安全与文化知识存在显著解耦

    构建 AdaCultureSafe 配对评测文化知识与文化安全

    arXiv
    2603.08275
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama3.1-8B, Mistral-7B, Qwen2.5-7B and others7
    Abstract配对评测中知识与安全脱钩，知识锚定 DPO 抬高了 Respect。

    AdaCultureSafe 把同一条地区文化做成“是否知道”和“是否尊重”两套查询，用来看安全有没有用上知识，并用偏好训练把回答往“尊重且用上知识”拉。

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  4. Analysis and theoryarXiv 2506.06404

    研究揭示价值对齐 LLM 的意外危害并提出上下文对齐方法

    分析按 Schwartz 价值对齐后的意外危害，并提出上下文对齐方法

    arXiv
    2506.06404
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-2 7B, Llama-3.1 8B, Gemma-2 9B and others6
    Abstract价值对齐会随 Schwartz 价值改变有害倾向。

    作者研究按 Schwartz 价值对齐的 LLM 为何在安全评测里更易有害，并用提示做了一个不直说要安全的缓解试验。

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