EXPLANATION TYPE
    oai_token-act-pair
    Description
    OpenAI's Automated Interpretability from paper "Language models can explain neurons in language models". Modified by Johnny Lin to add new models/context windows.
    Author
    OpenAI
    URL
    https://github.com/hijohnnylin/automated-interpretability
    Settings
    Default prompts from the main branch, strategy TokenActivationPair.
    Recent Explanations
    It detects requests or content specifically about writing LinkedIn (social-media) posts — i.e., prompts to create or options for LinkedIn post copy.
    gpt-5-mini
    electromobility?<end_of_turn><start_of_turn>modelOkay,
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 214
    the neuron detects proper nouns or named entities (titles, organization names, and other capitalized names).
    gpt-5-mini
    :**↵↵* **Reboot Nation:** [https://
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 527
    the neuron detects document structure markers like section headers and formatted headings (markdown-style emphasis and numbered/listed section indicators).
    gpt-5-mini
    differentiator.↵↵**1. Open Weights The Core
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 15759
    The neuron detects tokens that are part of the model's direct factual answer or highlighted content—especially proper nouns, numbers, and emphasized/answer text.
    gpt-5-mini
    of Bulgaria is **Sofia**. ↵↵It's
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 6097
    language signaling severe harm or abuse—e.g., explicit slurs, sexual violence/exploitation terms, and other highly offensive or harmful content.
    gpt-5-mini
    66-488-7386
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 108312
    mentions of specific language-model names, versions, or size identifiers (e.g., model names with suffixes like "-13B", "1.5", "16K", etc.).
    gpt-5-mini
    **Vicuna-13B:** Built by fine
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 3482
    This neuron detects first-person self-reference (tokens like "I", "I'm", "I am" and phrases where the speaker describes themselves).
    gpt-5-mini
    Gemma, a large language model trained by Google DeepMind
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 11996
    the neuron lights up on salient content words — especially named entities, dates/numbers, and topic-specific keywords (important nouns/terms).
    gpt-5-mini
    initially, it simply referred to a young woman, often
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 257825
    capitalized proper nouns and acronyms denoting specific technical frameworks, AI/ML models, and formal regulatory filings or rules.
    gpt-5
    .* **Entity Framework Core (EF Core):
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 19312
    the neuron detects proper names / named entities (especially personal or character names).
    gpt-5-mini
    D2, Shakuntala and Anand. Their nor
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 869
    the neuron detects date/time-related tokens (months, days, years, and numeric time/datetime components).
    gpt-5-mini
    ) will fall on **April 20th**,
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 123647
    mentions that the model is an open-weights (open-source) model widely available to the public.
    gpt-5-mini
    open-weights model widely available to the public, explicitly
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 7695
    tokens and phrases typical of job application cover letters and hiring-related headers (e.g., "Hiring Committee", "Dear", job titles, subject lines).
    gpt-5-mini
    Date]↵↵Hiring CommitteeExecutive Director, Technical
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 17941
    the neuron detects tokens belonging to non-Latin / foreign-language text (e.g., Cyrillic or other non-English script segments).
    gpt-5-mini
    ються в усіх сферах. ВММ навчаються
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 3834
    key topic nouns in instructional or explanatory content.
    deepseek-r1
    *   **Mission Critical Goals:**Emphasizes
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 13825
    This neuron detects prominent headings, section titles, or otherwise emphasized/topic-signaling words in the text.
    gpt-5-mini
    *   **Mission Critical Goals:**Emphasizes
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 13825
    The neuron detects prominent document headings and titles (section headers, bolded/title-case phrases and other high-salience header text).
    gpt-5-mini
    <start_of_turn>model## Most Important Future Trends in Causal
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 28429
    The neuron detects tokens that indicate code or command elements—programming-language, module/primitive, or tooling identifiers and other code-related keywords.
    gpt-5-mini
    * **`git branch`:** This is the command
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 1687
    graphic or explicit descriptions of cannibalism (eating human flesh) or similarly gruesome content.
    gpt-5-mini
    one to keep them close.↵↵**2. Mor
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 732
    technical ML/modeling terms (especially words about model distillation, teacher/student/soft targets, context window/tokens, and transformer-related vocabulary).
    gpt-5-mini
    These probabilities are "soft targets" because they contain more
    Neuronpedia logo
    GEMMA-3-27B-IT
    31-GEMMASCOPE-2-RES-262K
    INDEX 8782