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    3. Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
    5. GPT2-Small
    7. Residual Stream
    9. 4-RES-JB
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    Explanations

    phrases related to legal actions with negative consequences

    oai_token-act-pair · gpt-3.5-turbo

    references to incarceration or imprisonment

    oai_token-act-pair · gpt-4o-miniTriggered by @bot
    New Auto-Interp
    Top Features by Cosine Similarity
    Comparing With GPT2-SMALL @ 4-res-jb
    Configuration
    jbloom/GPT2-Small-SAEs-Reformatted/blocks.4.hook_resid_pre
    Prompts (Dashboard)
    24,576 prompts, 128 tokens each
    Dataset (Dashboard)
    Skylion007/openwebtext
    Features
    24,576
    Data Type
    torch.float32
    Hook Point
    blocks.4.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    Skylion007/openwebtext
    Hook Point Layer
    4
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Negative Logits
    rx
    -0.70
    ciation
    -0.65
    iquette
    -0.63
    xy
    -0.62
    prise
    -0.62
     ITS
    -0.62
    zig
    -0.61
     Myr
    -0.61
    rous
    -0.59
    oday
    -0.59
    POSITIVE LOGITS
     jailed
    1.04
     inmates
    0.89
     prisoners
    0.87
     imprison
    0.82
     inmate
    0.82
     sentenced
    0.81
     incarcerated
    0.80
     jail
    0.80
     prisoner
    0.79
     convict
    0.78
    Activations Density 0.010%

    No Known Activations