长上下文下大模型隐私泄露和个性化能力反而下降，揭示了潜在的性能瓶颈。
Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization
- 构建大规模基准PAPerBench，测试不同上下文长度下的模型表现
- 上下文越长，个性化与隐私保护效果均显著下降
- 提出注意力稀释理论解释该现象，适合关注隐私与可扩展性的研究者
大型语言模型（LLMs）正越来越多地应用于涉及隐私保护和个人化场景，但上下文长度对隐私泄露与个人化效果的影响仍不明确。本文提出一个大规模基准PAPerBench，系统研究上下文长度增加对个人化质量与隐私保护的影响。该基准包含约2.9万个样本，上下文长度覆盖1K至256K tokens，共生成37.7万道评估问题。在多种场景下联合评估个人化性能与隐私风险，实现对长上下文模型行为的可控分析。对主流LLMs的广泛评估显示，随着上下文长度增加，个人化与隐私保护能力均持续下降。进一步的理论分析表明，这是固定容量Transformer中软注意力机制固有的注意力稀释现象所致。实证与理论结果共同揭示当前模型存在普遍的‘长上下文、少聚焦’缩放鸿沟。相关代码与数据已开源，支持可复现研究与未来探索。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly deployed in privacy-critical and personalization-oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large-scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality and privacy protection in LLMs. The benchmark comprises approximately 29,000 instances with context lengths ranging from 1K to 256K tokens, yielding a total of 377K evaluation questions. It jointly evaluates personalization performance and privacy risks across diverse scenarios, enabling controlled analysis of long-context model behavior. Extensive evaluations across state-of-the-art LLMs reveal consistent performance degradation in both personalization and privacy as context length increases. We further provide a theoretical analysis of attention dilution under context scaling, explaining this behavior as an inherent limitation of soft attention in fixed-capacity Transformers. The empirical and theoretical findings together suggest a general scaling gap in current models -- long context, less focus. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization. Code and data are available at https://github.com/SafeRL-Lab/PAPerBench
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