Important Dates

Submission due: September 2, 2026

ARR reviewed submission due: October 3, 2026

Notification of acceptance: October 9, 2026

Camera-ready papers due: October 19, 2026

Workshop: November 9, 2026

Overview

The 7th Workshop on Gender Bias in Natural Language Processing (GeBNLP 2026) will be co-located with the 5th AACL in Hengqin, China. The workshop will be held fully remotely.

The GeBNLP workshop serves as a leading venue for the study, evaluation, and mitigation of gender bias in natural language processing. As large language models (LLMs) become foundational to recent NLP applications, addressing how these systems represent and affect different gender, alongside intersecting demographic axes such as race, nationality, religion, and ability, remains a critical challenge for the AI community. While foundational progress has been made in algorithmic debiasing and balanced data collection, recent large-scale evaluations reveal that state-of-the-art models continue to exhibit persistent stereotypes and confidence disparities across intersectional identities (Siddique et al., 2024; Savoldi et al., 2024). Our workshop acts as a multidisciplinary bridge, enabling researchers to define shared standards for tasks and metrics that ensure technical advancements are grounded in the social and ethical realities of systemic harm (Dai et al., 2024).

Topics of Interest

We invite submissions on a wide range of topics related to gender bias in NLP, including but not limited to:

Measurement and Evaluation: New metrics and datasets for quantifying bias in LLMs, machine translation, and multimodal systems.
Mitigation Strategies: Technical approaches to debiasing (e.g., fine-tuning, adapter-based methods, or prompting strategies).
Multilingual and Cross-Cultural Perspectives: Bias in low-resource languages or non-Western cultural contexts.
Intersectionality: Research exploring how gender bias intersects with race, disability, age, or nationality.
Ethical and Legal Frameworks: Policy implications and the human-in-the-loop role in auditing AI systems.

Authors are encouraged to go beyond binary gender definitions and discuss how their work addresses the complexity of intersecting stereotypes and the diverse demographic contexts involved.

Submission Guidelines

Long Papers: Up to 8 pages (excluding references).
Short Papers: Up to 4 pages (excluding references).
Non-Archival Submissions: Authors may opt for non-archival submission, allowing work of standard conference quality to be presented without being published in the official proceedings.
Submission Link: direct submission or ARR commitment. Blind submission is required.

Organizers

Giuseppe Attanasio, Instituto de Telecomunicações, Portugal
Christine Basta, Alexandria University & HiTZ, University of the Basque Country
Marta R. Costa-jussà, FAIR, Meta
Agnieszka Faleńska, University of Stuttgart
Christian Hardmeier, IT University of Copenhagen
Vera Neplenbroek, University of Amsterdam
Debora Nozza, Bocconi University
Karolina Stańczak, ETH AI Center

References

Dai, Y., Gu, H., Wang, Y. and Wang, X., 2024, November. Mitigate extrinsic social bias in pre-trained language models via continuous prompts adjustment. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 11068–11083).
Siddique, Z., Turner, L. and Anke, L.E., 2024, November. Who is better at math, jenny or jingzhen? Uncovering stereotypes in large language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18601–18619).
Savoldi, B., Papi, S., Negri, M., Guerberof-Arenas, A. and Bentivogli, L., 2024, November. What the harm? Quantifying the tangible impact of gender bias in machine translation with a human-centered study. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 18048–18076).

Contact Info

Please send any questions about the workshop to gebnlpworkshop@gmail.com