Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions
Score
66首次系统提出自然语言处理需转向长期纵向测量，填补了现有评测在长期风险上的空白。
观点有理论支撑，但属于立场论述，缺乏实证案例和数据支持。
为AI对齐和评测设计提供了可操作的新思路，具有实际应用潜力。
针对大模型长期影响这一前沿问题，当前关注度高，时效性强。
作为arXiv预印本，未经同行评审，权威性有限。
Base 73 × T2 Source weight ×0.9 = 66
Curation line 70Below curation line
Why selected
The paper introduces a novel perspective filling a gap in NLP evaluation for longitudinal risks, yet remains a positional argument without empirical validation.
AI commentary
「As an editor, I find this paper hits a blind spot in current LLM evaluation—most tests focus on short-term performance, while long-term psychological and behavioral effects on users are under-examined. It is especially valuable for NLP researchers and AI safety practitioners, reminding us that evaluation should shift from text quality to behavioral outcomes and offering a new lever for alignment. However, the proposal remains at an advocacy stage, and we look forward to follow-up empirical studies.」
Quick overview
This paper argues that large language models, due to their human-like qualities and rapid integration into daily life, may introduce longitudinal risks—cognitive, developmental, and socio-affective changes that do not surface in short-term interactions but can have lasting effects. The authors call for NLP to shift from static, short-term evaluations to long-term measurements of behavioral changes, drawing on social science methods to build a diachronic understanding of human-model interactions. This approach enables online detection of problematic behaviors and should be integrated into alignment frameworks to mitigate long-term risks.
- Longitudinal risks such as cognitive, developmental, and socio-affective changes are invisible in short-term evaluations.
- The paper calls for NLP to adopt social science measurements and pivot to long-term behavioral monitoring.
- This approach enables online detection of problematic behaviors and supports alignment frameworks to reduce long-term user risks.
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