Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model's output, a straightforward approach is to incorporate past user data into the language model prompt, but this approach can result in lengthy inputs exceeding limitations on input length and incurring latency and cost issues. Existing approaches tackle such challenges by selectively extracting relevant user data (i.e. selective retrieval) to construct a prompt for downstream tasks. However, retrieval-based methods are limited by potential information loss, lack of more profound user understanding, and cold-start challenges. To overcome these limitations, we propose a novel summary-augmented approach by extending retrieval-augmented personalization with task-aware user summaries generated by LLMs. The summaries can be generated and stored offline, enabling real-world systems with runtime constraints like voice assistants to leverage the power of LLMs. Experiments show our method with 75% less of retrieved user data is on-par or outperforms retrieval augmentation on most tasks in the LaMP personalization benchmark. We demonstrate that offline summarization via LLMs and runtime retrieval enables better performance for personalization on a range of tasks under practical constraints.
University of Southern California Information Sciences Institute（南加州大学信息科学研究所）；School of Computer Science, Wuhan University（武汉大学计算机学院）；University of California, Davis（加州大学戴维斯分校）
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；National University of Defense Technology（国防科技大学）；Chongqing University of Technology（重庆理工大学）
Personalized Graph-Based Retrieval for Large Language Models
University of California Santa Cruz（加州大学圣克ruz分校）；Meta AI；Adobe Research（Adobe研究）；University of Oregon（俄勒冈大学）；University of Southern California（南加州大学）；Cisco AI Research（思科人工智能研究）
ToolSpectrum : Towards Personalized Tool Utilization for Large Language Models
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；University of Science and Technology Beijing（北京科技大学）；The Chinese University of Hong Kong（香港中文大学）；Baidu Inc.（百度公司）
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
Tutu AI（图图人工智能）；University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Michigan（密歇根大学）；The Hong Kong Polytechnic University（香港理工大学）
NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment
VNU University of Engineering and Technology（越南工程大学）；Japan Advanced Institute of Science and Technology（日本先进科学研究院）；Center for Juris-Informatics, ROIS-DS（法律信息中心，ROIS-DS）；Research and Development Center for Large Language Models, NII（大型语言模型研发中心，NII）
A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback
Faculty of Artificial Intelligence in Education（人工智能教育学院）；Central China Normal University（中国中部师范大学）；School of Electrical and Electronic Engineering（电气与电子工程学院）；Hubei University of Technology（湖北工业大学）
University of Colorado（科罗拉多大学）；University of Edinburgh（爱丁堡大学）
Many NLG tasks such as summarization, dialogue response, or open domain question answering focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user's intent or context of work is not easily recoverable based solely on that source text -- a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial to…
Nanjing University（南京大学）；Samsung Research America（三星美国研究中心）；Kno.e.sis Center, Wright State University（莱特州立大学Kno.e.sis研究中心）
The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. The magnitude of semantic data, in particular the large number of triples describing an entity, could overload users with excessive amounts of information. This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively. We focus on this prominent topic of entity summarization, and our research objective is to present the…
