Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong Park. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Large Language Models (LLMs) excel at tackling various natural language tasks. However, due to the significant costs involved in re-training or fine-tuning them, they remain largely static and difficult to personalize. Nevertheless, a variety of applications could benefit from generations that are tailored to users' preferences, goals, and knowledge. Among them is web search, where knowing what a user is trying to accomplish, what they care about, and what they know can lead to improved search experiences. In this work, we propose a novel and general approach that augments an LLM with relevant context from users' interaction histories with a search engine in order to personalize its outputs. Specifically, we construct an entity-centric knowledge store for each user based on their search and browsing activities on the web, which is then leveraged to provide contextually relevant LLM prompt augmentations. This knowledge store is light-weight, since it only produces user-specific aggregate projections of interests and knowledge onto public knowledge graphs, and leverages existing search log infrastructure, thereby mitigating the privacy, compliance, and scalability concerns associated with building deep user profiles for personalization. We validate our approach on the task of contextual query suggestion, which requires understanding not only the user's current search context but also what they historically know and care about. Through a number of experiments based on human evaluation, we show that our approach is significantly better than several other LLM-powered baselines, generating query suggestions that are contextually more relevant, personalized, and useful.

Jinheon Baek, Alham Aji, Amir Saffari. Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023). 2023.

Simon A. Aytes, Jinheon Baek, Sung Ju Hwang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Jinheon Baek, Soyeong Jeong, Minki Kang, Jong Park, Sung Hwang. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Soyeong Jeong, Kangsan Kim, Jinheon Baek, Sung Ju Hwang. Findings of the Association for Computational Linguistics: ACL 2025. 2025.