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    AI

    Investor Says LLMs Lack Self-Calibration on Errors

    Pseudonymous a16z investor @alth0u states LLMs never self-correct after spotting errors.

    AL
    1 Source, 29d ago, first seen 29d ago

    TLDR

    @alth0u posted that LLMs face a fundamental know thyself calibration problem. The account described how humans notice miscalibration during deeper review and then create tooling or adjust their process to avoid repeats. LLMs do not perform this step. The post added that much of the work involves scanning traces and converting them into outputs. The tweet came from an account positioned as a general partner at a16z focused on AI models, RL, alignment, and venture commentary.

    Combined views

    969

    1 Source, first seen 29d ago

    Combined views

    969

    1 Source, first seen 29d ago

    15 likes
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    15 likes
    2 comments

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    2 comments

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    1 Source

    @alth0ullm's have a fundamental "know thyself" calibration problem if i answer any question and i realize i keep being miscalibrated upon doing deeper dives i will build tooling and/or change my structural approach to prevent this in the future llm's do not do this most of my job is scanning traces and then turning them into tools or refactoring libs because of what i find like instead of remembering not to walk into a weird rock in my living room, i have to add things around it or move it

    1 Source

    @alth0ullm's have a fundamental "know thyself" calibration problem if i answer any question and i realize i keep being miscalibrated upon doing deeper dives i will build tooling and/or change my structural approach to prevent this in the future llm's do not do this most of my job is scanning traces and then turning them into tools or refactoring libs because of what i find like instead of remembering not to walk into a weird rock in my living room, i have to add things around it or move it