LLM科学影响力随发布时间快速衰减，新模型寿命缩短超四成。
The Shrinking Lifespan of LLMs in Science
- 用发布年份预测模型峰值与寿命，优于架构、规模等特性。
- 每代新模型的达到峰值时间缩短27%，生命周期缩短23%。
- 揭示了模型过时对可复现性与迁移成本的隐性影响。
缩放定律描述了语言模型能力随算力与数据的增长，却未涉及模型发布后持续有效的时间。本文引入‘达到峰值时间’与‘寿命’作为模型过时度量，分析62个LLM在108,000多篇引用论文（2019–2025）中的科学采纳轨迹，区分主动使用与背景引用，还原出引用数无法揭示的模型生命周期。结果发现，模型寿命更受发布年份影响：发布年份比架构、开放性或规模更能预测达到峰值时间与寿命。LLM采纳呈倒U型曲线（发布后上升、达峰、下降），但该模式正迅速压缩：每代新发布模型的达到峰值时间缩短27%，寿命缩短23%（p < 0.001），且在最小年龄阈值和模型规模控制下仍稳健。这些采纳动态在缩放定律中不可见，提示单一模型专精可能为贬值投资，带来可复现性与迁移成本的代价。
原文摘要 · Abstract (English)
Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released. We introduce time-to-peak and lifespan as measures of model obsolescence and use them to characterize the scientific adoption trajectories of 62 LLMs across more than 108k citing papers (2019-2025), separating active adoption from background citation to recover per-model trajectories that citation counts cannot resolve. We find that a model's longevity is shaped more by when it was released than by its characteristics: release year predicts time-to-peak and lifespan more strongly than architecture, openness, or scale. LLM adoption follows an inverted-U curve (rising after release, peaking, and then declining), but this pattern is rapidly compressing. Each successive release year is associated with a 27% shorter time-to-peak and a 23% shorter lifespan ($p < 0.001$), robust to minimum-age thresholds and controls for model size. These adoption-side dynamics are invisible to scaling laws and suggest that specialization on any single model may be a depreciating investment, with costs falling on reproducibility and migration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。