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LLM self-models are constructed indirectly from training data, analysis finds

By PulseAugur Editorial · [1 sources] ·

A recent analysis suggests that large language models do not possess true self-awareness or an internal "self-model" in the way humans do. Instead, their apparent understanding of themselves is constructed indirectly from the vast datasets they are trained on. This means that any "self-reflection" observed in an LLM is a reflection of the data's content about similar concepts, rather than genuine introspection. AI

IMPACT Challenges the notion of LLM self-awareness, suggesting current models reflect data rather than possess internal states.

RANK_REASON The cluster contains a research paper analyzing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on LessWrong (AI tag) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM self-models are constructed indirectly from training data, analysis finds

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The cluster contains a research paper analyzing LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. LessWrong (AI tag) TIER_1 English(EN) · Alexandre Variengien ·

    LM self-models are built from indirect sources

    <p><span style="white-space: pre-wrap;">Imagine you ask a language model "What is your personality?" as the first message of a conversation, with no system prompt. The model has no data about its own behavior in its context, so its answer can only rely on cached, zero-shot knowle…

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