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    3. Apollo Research · Taylor · Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
    5. GPT2-Small
    7. Residual Similar CE E2E Recon
    9. 6-RES_SCEFR-AJT
    11. 13301
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    Explanations

    phrases related to marathon running and events such as the Boston Marathon

    oai_token-act-pair · gpt-3.5-turbo
    New Auto-Interp
    Top Features by Cosine Similarity
    Configuration
    neuronpedia/gpt2-small__res_scefr-ajt/6-res_scefr-ajt
    Prompts (Dashboard)
    12,288 prompts, 128 tokens each
    Dataset (Dashboard)
    Skylion007/openwebtext
    Features
    46,080
    Data Type
    torch.float32
    Hook Point
    blocks.6.hook_resid_pre
    Architecture
    standard
    Context Size
    128
    Dataset
    apollo-research/Skylion007-openwebtext-tokenizer-gpt2
    Hook Point Layer
    6
    Activation Function
    relu
    Embeds
    IFrame
    Link
    Not in Any Lists

    No Comments

    Neuron Alignment
    Index
    Value
    % of L₁
    326
    +0.16
    0.6%
    288
    +0.15
    0.6%
    302
    +0.12
    0.5%
    Correlated Neurons
    Index
    P. Corr.
    Cos Sim.
    288
    +0.16
    0.07
    326
    +0.15
    0.05
    343
    +0.12
    0.04
    Negative Logits
    ortium
    -0.85
     suscept
    -0.84
    metics
    -0.83
    imated
    -0.81
    utical
    -0.81
     Debor
    -0.80
    umin
    -0.80
    orum
    -0.78
     Aires
    -0.77
    choes
    -0.76
    POSITIVE LOGITS
     Runner
    1.14
     runner
    1.04
    NING
    0.94
     runners
    0.90
     marathon
    0.90
     Polo
    0.90
    athon
    0.86
    fruit
    0.85
    runner
    0.84
    runners
    0.84
    Activations Density 0.473%

    No Known Activations