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LLMs fail to control internal representations under stricter neurofeedback conditions

A new study challenges previous findings on LLMs' ability to control their internal representations, a key aspect for AI safety and metacognition. Researchers redesigned the neurofeedback paradigm to ensure control targets were "privileged," meaning they could not be inferred by external observers, mirroring human neuroscience experiments. Under this stricter condition, the LLMs failed to demonstrate reliable control over these privileged internal states, suggesting prior claims of control might have been based on superficial mechanisms rather than genuine internal access. AI

IMPACT This research refines methods for evaluating LLM metacognition, crucial for developing more robust and trustworthy AI systems.

RANK_REASON Academic paper detailing a new experimental setup and findings regarding LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

LLMs fail to control internal representations under stricter neurofeedback conditions

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Academic paper detailing a new experimental setup and findings regarding LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    In-Context Neurofeedback: Can LLMs Control Their Internal Representations through Privileged Access?

    Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study applied neurofeedback to LLMs and claimed that they can control their internal representations. However, the reported control m…