Active and Passive LLM Personalization: When to personalize and what role do humans play in the personalization process?

EMNLP 2025 in Suzhou, China from November 5-9

Call for Papers

Large language models (LLMs) have demonstrated remarkable capabilities in various NLP tasks. However, the extent to which these models can and should adapt to individual users' needs remains an open question. Therefore, this workshop will focus on the personalization of LLMs to meet individual user's needs and preferences. From the user's perspective the personalization process can be either passive, where the LLM learns from observing user behavior, or active, where the user directly guides the personalization. The questions of how to personalize effectively, when to personalize, which personalization paradigm to apply (active vs. passive) remain open questions that are important to address. Further, active and passive personalization each have several challenges and open questions in their own right.

Successfully answering these kinds of questions inherently requires an interdisciplinary approach, combining expertise from a wide variety of fields, such as: NLP, human-computer interaction, linguistics, cognitive science, behavioral science, psychology, ethics, etc. Thus, the workshop will promote interdisciplinary research necessary for effective, user-first personalization, driving towards solving the challenges of passive and active personalization.

🏆 Best Student Paper Award: We are excited to award the best student paper with an iPad!

Topics

We invite submissions related, but not limited, to the following topics in the space of active and passive LLM personalization:

Algorithmic approaches (user persona/profile creation, in-context learning, RAG, parameter tuning, user representation learning, user-specific reasoning, etc.)
Evaluation methods for offline and online use cases
Potential risks of personalized LLMs (creating an echo chamber, limiting user exploration)
User studies or surveys to understand when, where, and how active vs. passive personalization is effective
Dataset creation and curation (collecting and curating a dataset from real users, creating and validating a synthetic dataset, identifying salient samples for personalization, etc.)

Important Dates

ARR submission deadline: May 19, 2025
ARR commitment deadline: September 4, 2025 (Submission link)
Direct submission deadline: August 1 (Submission link)
Notification of acceptance: September 17 (tentative)
Camera ready deadline: September 21 (tentative)
Workshop dates: To be announced. Collocated with EMNLP (Main Conference is November 5-9, 2025)

Guidelines

We follow the standard *ACL template, and welcome short (up to 4 pages) and long (up to 8 pages) papers. References and appendices are not included in the page limits.
We welcome direct submissions via OpenReview, as well as submissions made through ARR. For submissions made through ARR, we will calibrate scores such that they are suitable for a workshop submission (for example if initially intended as a full conference paper). Please include both the original submission and all ARR reviews when uploading ARR submissions.

Submit directly via OpenReview: https://openreview.net/group?id=EMNLP/2025/Workshop/PALS

See Important Dates for deadlines. Papers will be non-archival and can be published on preprint servers, such as ArXiv.
We plan to have an archival and a non-archival option. Please check back for more details closer to the submission deadline.