理解和测量LLM行为中的认知萎缩
Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour
AI总结：
针对LLM在心理健康支持中缺乏过程行为评估的问题，提出认知萎缩概念及基准，通过临床标注和专家评估揭示模型普遍存在中度至高度萎缩行为。
摘要：
近期涉及LLM用于心理健康支持的事件揭示了一个关键的评估空白：表面安全评分无法捕捉模型在长时间、现实且情感敏感的交互中的行为。现有基准衡量知识、安全性或静态响应质量，但忽略了LLM交互是否帮助用户保持反思、应对和自主决策。我们将这一缺失维度形式化为认知萎缩，这是一种AI介导的心理健康支持中不同于安全性和有用性的过程级行为度量。为测量它，我们引入了认知萎缩基准，这是一个基于临床的基准，由1,576个完全人工生成的咨询对话、15,680轮次和来自五个LLM的42,230个响应构建而成。三位临床和神经心理学专家开发了一个包含用户上下文、响应行为和全局风险标志的20属性模式；六名经过培训的临床评审员应用该模式并附上基于跨度的证据，产生了5,324个评审判断。我们进一步引入了用户输入风险指数、认知萎缩风险指数和轨迹摘要。在五个LLM中，模型在单轮和多轮设置中表现出一致的中度至高度萎缩对齐行为。虽然模型通常对明显的安全线索做出响应，但当用户寻求解决方案或决策时，它们的适应性较差。主要的重复模式是指导性建议、问题解决、推荐响应、话题转移以及可能强化依赖而非反思的验证形式。我们的工作使认知萎缩变得可测量，并为审计敏感LLM对话中的模型行为提供了基础。
英文摘要：
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 response quality, but miss whether LLM interactions help users keep reflecting, coping, and making decisions themselves. We formalize this missing dimension as COGNITIVE ATROPHY, a process-level behavioural measure in AI-mediated mental-health support distinct from safety and helpfulness. To measure it, we introduce COGNITIVE ATROPHY BENCH, a clinically grounded benchmark built from 1,576 fully human-generated counseling conversations, 15,680 turns, and 42,230 responses from five LLMs. Three clinical and neuropsychology experts developed a 20-attribute schema spanning user context, response behaviour, and global risk flags; six trained clinical reviewers applied it with span-grounded evidence, producing 5,324 reviewer judgments. We further introduce the User-Input Risk Index (UIRI), the Cognitive Atrophy Risk Index (ARI), and trajectory summaries. Across five LLMs, models show a consistent moderate-to-high level of atrophy-aligned behaviour across single and multi-turn settings. While models generally respond to overt safety cues, they adapt less reliably when users seek solutions or decisions. The dominant recurring patterns are directive advice, problem-solving, recommendation responses, topic shifts, and forms of validation that may reinforce dependence rather than reflection. Our work makes COGNITIVE ATROPHY measurable and provides a foundation for auditing model behaviour in sensitive LLM conversations.
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