Large language model (LLM) personalization typically relies on modeling each user in isolation, conditioning on their historical interactions to adapt model behavior. However, this user-centric formulation overlooks the collective knowledge shared across users, limiting generalization for users with sparse histories and amplifying overfitting for those with highly skewed behaviors. We argue that effective personalization requires leveraging both individual preferences and population-level patterns. To this end, we propose LoGo, a Local–Global knowledge framework that augments user-specific signals with a global knowledge encoding collective behavioral trends. LoGo models global knowledge through a temporally evolving process that captures how population-wide preferences change over time, and a community-aware structure that organizes users into coherent groups with shared interests. To balance potentially conflicting local and global signals, LoGo employs a mediator module that adaptively fuses the two knowledge sources. Experiments on five personalization benchmarks show that LoGo consistently enhances personalization quality, outperforming existing methods by improving generalization in users with limited histories and mitigating bias in users with abundant histories. These results demonstrate the central role of collective knowledge in advancing LLM personalization. Our code is publicly available at https://github.com/Zehong-Wang/LoGo.

Extraction of experimental procedures from human language in scientific literature and patents into actionable sequences in robotics language holds immense significance in scientific domains. Such an action extraction task is particularly challenging given the intricate details and context-dependent nature of the instructions, especially in fields like chemistry where reproducibility is paramount. In this paper, we introduce ActionIE, a method that leverages Large Language Models (LLMs) to bridge this divide by converting actions written in natural language into executable Python code. This enables us to capture the entities of interest, and the relationship between each action, given the features of Programming Languages. Utilizing linguistic cues identified by frequent patterns, ActionIE provides an improved mechanism to discern entities of interest. While our method is broadly applicable, we exemplify its power in the domain of chemical literature, wherein we focus on extracting experimental procedures for chemical synthesis. The code generated by our method can be easily transformed into robotics language which is in high demand in scientific fields. Comprehensive experiments demonstrate the superiority of our method. In addition, we propose a graph-based metric to more accurately reflect the precision of extraction. We also develop a dataset to address the scarcity of scientific literature occurred in existing datasets.

Chemical reactions, as a core entity in the realm of chemistry, hold crucial implications in diverse areas ranging from hands-on laboratory research to advanced computational drug design. Despite a burgeoning interest in employing NLP techniques to extract these reactions, aligning this task with the real-world requirements of chemistry practitioners remains an ongoing challenge. In this paper, we present Reaction Miner, a system specifically designed to interact with raw scientific literature, delivering precise and more informative chemical reactions. Going beyond mere extraction, Reaction Miner integrates a holistic workflow: it accepts PDF files as input, bypassing the need for pre-processing and bolstering user accessibility. Subsequently, a text segmentation module ensures that the refined text encapsulates complete chemical reactions, augmenting the accuracy of extraction. Moreover, Reaction Miner broadens the scope of existing pre-defined reaction roles, including vital attributes previously neglected, thereby offering a more comprehensive depiction of chemical reactions. Evaluations conducted by chemistry domain users highlight the efficacy of each module in our system, demonstrating Reaction Miner as a powerful tool in this field.