Personalized text generation aims to infer users’ writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics. Existing solutions primarily adopt two paradigms: retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT). While these approaches have advanced the field, they suffer from two critical limitations: (1) the entanglement of content semantics and stylistic patterns in historical texts impedes accurate modeling of user-specific writing preferences; and (2) scalability challenges arising from both RAG’s inference latency by retrieval operations and PEFT’s parameter storage requirements for per user model. To overcome these limitations, we propose StyleVector, a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation space, enabling style-steered generation during inference without requiring costly retrieval or parameter storage. Comprehensive experiments demonstrate that our framework achieves a significant 8% relative improvement in personalized generation while reducing storage requirements by 1700 × over PEFT method.

Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, their susceptibility to security threats has been largely overlooked. In this work, we reveal that the introduction of LLMs into recommendation models presents new security vulnerabilities due to their emphasis on the textual content of items. We demonstrate that attackers can significantly boost an item’s exposure by merely altering its textual content during the testing phase, without requiring direct interference with the model’s training process. Additionally, the attack is notably stealthy, as it does not affect the overall recommendation performance and the modifications to the text are subtle, making it difficult for users and platforms to detect. Our comprehensive experiments across four mainstream LLM-based recommendation models demonstrate the superior efficacy and stealthiness of our approach. Our work unveils a significant security gap in LLM-based recommendation systems and paves the way for future research on protecting these systems.

“尽管大语言模型(LLM)在自然语言处理领域取得巨大成功,但是伴随其千亿级参数 规 模 的 训 练 也 产 生 了 巨 大 的 计 算 成 本 。 小 规 模 大 语 言 模 型(SLLM)作 为 低 资 源场景下实现LLM部署的可替代方案,任务处理能力与LLM尚存在明显差距。尽管上下文学习(ICL)等提示方法在一定程度上提升了SLLM的问题处理能力,但基于人工构建的提示往往需要参与者具备特定的专业领域知识,这给LLM的普适推广带来了挑战。针对以上问题,本文提出了一个基于SLLM的问题推理框架,通过在推理路径生成和答案生成两个阶段之间引入基于逐步语义验证器(SSVRP)的推理路径排序选择机制,在无人干预情况下实现SLLM推理能力提升。实验结果表明,SSVRP有效地增强了SLLM的推理性能,在4个推理任务中的平均准确率分别达到了54.3%,90.6%,64.3%和63.7%,并在其中3个推理任务中都取得了最新的SOTA结果。”