BoF @ EMNLP 2026

About the session

Description from the EMNLP 2026 program.

Style plays a central role across the full NLP pipeline: the stylistic diversity of training data influences model robustness, users expect generated texts in a helpful or personalized style, evaluation on texts with limited stylistic variation can obfuscate a model’s ability to function in the wild, and applications like writing assistance, AI-generated text detection, anonymization and authorship verification rely on style features. With this BoF session we want to connect researchers that are interested in how style can be modeled, measured and applied across the NLP pipeline.

We will open the session with a 15-minute introduction to the topic and open questions, and will then encourage informal discussions around topics participants are interested in. They might cover style in language modeling and generation (personalization, style steering/transfer), measuring and explaining style, applications (authorship attribution and verification, AI-text detection, anonymization, forensic and literary analysis), and linguistic perspectives on style.

Style and New Missions for NLP Research

Style is central to New Missions for NLP Research: Language models encounter diverse registers, dialects and individual writing styles, and generate texts with stylistic prints of their own. Current benchmarks may capture only a narrow range of stylistic features or definitions of style, leaving open how to model and evaluate style across domains, tasks and social contexts. These questions are also relevant to applications such as writing assistance, personalization and chatbots, where style also depends on humans’ subjective preferences. Studying linguistic style thus contributes both to understanding human language and to developing language technologies that support diverse needs.

Continue the conversation

The discussion continues at StyleMatters, the first workshop on linguistic style in NLP. See the call for papers and the shared task.