不看任务准确率，专测AI模仿你文风的“神似度”，PersonalBench填补了个性化写作评测空白
arXiv:2608.19746v1 Announce Type: new Abstract: Personalized text generation aims to make LLMs write in a specific individual's style, yet existing be…
用作者身份验证理论评估LLM风格个性化，揭示模型模仿个人文风时的真实差距与评测盲点。
arXiv:2604.26460v2 Announce Type: replace Abstract: Stylistic personalization - making LLMs write in a specific individual's style, rather than merely…
元学习+LoRA让大模型快速适应跨域偏好，个性化调校从此更聪明高效。
arXiv:2608.12389v1 Announce Type: new Abstract: Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen con…
把个性化奖励拆解成可解释概率偏好，不确定性建模让LLM对齐更稳更准。
arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared …
利用用户击键行为与语义线索，实现大模型对用户领域专业度的个性化响应
arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization method…
用自然语言推理约束验证，解决LLM个性化评估中表面匹配和计算成本问题，新范式更可解释。
arXiv:2606.16368v1 Announce Type: cross Abstract: Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle …
别再迷信合成数据，这项研究直指LLM个性化在真实用户场景下的性能短板，呼唤以人为中心的评估。
arXiv:2606.06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abiliti…
提出学习紧凑用户表示替代传统检索，实现大规模LLM个性化，效率与效果兼得
arXiv:2606.04547v1 Announce Type: cross Abstract: Personalizing large language models requires adapting model behavior to individual users while prese…
突破单一行为模式，提出层次化用户建模新框架，提升LLM个性化效果。
arXiv:2606.02300v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet per…