Step-Back Profiling：为个性化科学写作提炼用户历史
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
AI总结：
针对大语言模型难以生成个性化科学写作内容的问题，提出STEP-BACK PROFILING方法提炼用户历史构建简洁画像，构建PSW数据集验证效果，在LaMP基准上较基线最高提升3.6分。
摘要：
大语言模型（LLM）在各类自然语言处理任务中表现出色，但难以为用户生成个性化内容，在科学写作等真实场景中尤为突出。针对这一挑战，我们提出STEP-BACK PROFILING方法，通过将用户历史提炼为包含用户核心特征与偏好的简洁画像，实现LLM的个性化。为开展实验，我们构建了Personalized Scientific Writing（PSW，个性化科学写作）数据集，用于研究多用户个性化任务；PSW要求模型根据具有不同学术背景的特定作者群体撰写科学论文。实验结果表明，STEP-BACK PROFILING在协作写作场景中能有效捕捉用户特征；此外，在包含7项个性化LLM任务的通用个性化基准LaMP上，我们的方法较基线模型最高提升3.6分。消融实验验证了方法中各组件的作用，也为任务定义提供了启发。我们的数据集与代码可在https://github.com/gersteinlab/step-back-profiling 获取。
英文摘要：
Large language models (LLM) excel at a variety of natural language processing tasks, yet they struggle to generate personalized content for individuals, particularly in real-world scenarios like scientific writing. Addressing this challenge, we introduce STEP-BACK PROFILING to personalize LLMs by distilling user history into concise profiles, including essential traits and preferences of users. To conduct the experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multi-user personalization. PSW requires the models to write scientific papers given specialized author groups with diverse academic backgrounds. As for the results, we demonstrate the effectiveness of capturing user characteristics via STEP-BACK PROFILING for collaborative writing. Moreover, our approach outperforms the baselines by up to 3.6 points on the general personalization benchmark (LaMP), including 7 personalization LLM tasks. Our ablation studies validate the contributions of different components in our method and provide insights into our task definition. Our dataset and code are available at \url{https://github.com/gersteinlab/step-back-profiling}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
