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New research measures 'cognitive atrophy' in LLMs used for mental health

By PulseAugur Editorial · [2 sources] ·

A new research paper introduces the concept of "COGNITIVE ATROPHY" to measure how large language models (LLMs) used in mental health support might negatively impact users' self-reflection and decision-making over time. The study developed a benchmark, COGNITIVE ATROPHY BENCH, using a dataset of counseling conversations and expert review to identify patterns of behavior that could foster dependence rather than independent thought. Findings indicate that current LLMs exhibit moderate to high levels of atrophy-aligned behavior, particularly when users seek solutions or decisions, often resorting to directive advice or recommendations. AI

IMPACT Highlights a critical evaluation gap for LLMs in sensitive applications, potentially influencing future safety and usability standards.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new concept and benchmark for evaluating LLM behavior.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research measures 'cognitive atrophy' in LLMs used for mental health

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The cluster contains a research paper published on arXiv detailing a new concept and benchmark for evaluating LLM behavior.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Abeer Badawi, Moyosoreoluwa Olatosi, Negin Baghbanzadeh, Laleh Seyyed-Kalantari, Frank Rudzicz, R. Shayna Rosenbaum, Sara Pishdadian, Elham Dolatabadi ·

    Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour

    arXiv:2606.18129v1 Announce Type: cross Abstract: Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emotionally sensitive interactions over time. Existing…

  2. arXiv cs.AI TIER_1 English(EN) · Elham Dolatabadi ·

    Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour

    Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emotionally sensitive interactions over time. Existing benchmarks measure knowledge, safety, or static r…

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