TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing

Colocated with the 2025 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2025)

About

Recent advances in Natural Language Processing, and the emergence of pretrained Large Language Models (LLM) specifically, have led to significant breakthroughs in language understanding, generation, and interaction, leading to increasing usage of the models in real-life tasks. However, these advancements come with risks, including potential breaches of privacy, the propagation of bias, copyright violation, and vulnerabilities to adversarial manipulation. The demand for trustworthy NLP solutions is pressing as the public, policymakers, and organizations seek assurances that NLP systems protect data confidentiality, operate fairly, and adhere to ethical principles.

This year, we are excited to host our TrustNLP workshop at NAACL 2025, aimed at fostering discussions on these pressing challenges and driving the development of solutions that prioritize trustworthiness in NLP technologies. The workshop aspires to bring together researchers from various fields to engage in meaningful dialogue on key topics such as fairness and bias mitigation, transparency and explainability, privacy-preserving NLP methods, and the ethical deployment of AI systems. By providing a platform for sharing innovative research and practical insights, this workshop seeks to bridge the gaps between these interconnected objectives and establish a foundation for a more comprehensive and holistic approach to trustworthy NLP.

Call for Papers

Topics

We invite papers which focus on different aspects of safe and trustworthy language modeling. Topics of interest include (but are not limited to):

  • Secure, Faithful & Trustworthy Generation with LLMs
  • Data Privacy Preservation and Data Leakage Issues in LLMs
  • Red-teaming, backdoor or adversarial attacks and defenses for LLM safety
  • Fairness, LLM alignment, Human Preference Elicitation, Participatory NLP
  • Toxic Language Detection and Mitigation
  • Explainability and Interpretability of LLM generation
  • Robustness of LLMs
  • Mitigating LLM Hallucinations & Misinformation
  • Fairness and Bias in multi-modal generative models: Evaluation and Treatments
  • Industry applications of Trustworthy NLP
  • Culturally-Aware and Inclusive LLMs
We welcome contributions that also draw upon interdisciplinary knowledge to advance Trustworthy NLP. This may include working with, synthesizing, or incorporating knowledge across expertise, sociopolitical systems, cultures, or norms.

Important Dates

  • January 30 February 7, 2025: Workshop Paper Due Date (Direct Submission via OpenReview)
  • February 20, 2025 Workshop Paper Due Date (Fast-Track)
  • March 1, 2025: Notification of Acceptance
  • March 10, 2025: Deadline for relevant NAACL Findings to submit non-archival (Direct submission via form, link)
  • March 10, 2025: Camera-ready Papers Due
  • April 8, 2025: Pre-recorded video due
  • Friday May 3-4, 2025: TrustNLP Workshop day

Submission Information

All submissions undergo double-blind peer review (with author names and affiliations removed) by the program committee, and they will be assessed based on their relevance to the workshop themes.

All submissions go through the OpenReview. To submit, use submission link.

Submitted manuscripts must be 8 pages long for full papers and 4 pages long for short papers. Please follow NAACL submission policies. Both full and short papers can have unlimited pages for references and appendices. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper. Template files can be found here.

We also ask authors to include a limitation section and broader impact statement, following guidelines from the main conference.

Fast-Track Submission

If your paper has been reviewed by ACL, EMNLP, EACL, or ARR and the average rating is higher than 2.5 (either average soundness or excitement score), The paper is qualified to be submitted on the fast track. In the appendix, please include the reviews and a short statement discussing what parts of the paper have been revised.

Non-Archival Option

NAACL workshops are traditionally archival. To allow dual submission of work, we are also including a non-archival track. If accepted, these submissions will still participate and present their work in the workshop. A reference to the paper will be hosted on the workshop website (if desired), but will not be included in the official proceedings. Please submit through OpenReview but indicate that this is a cross submission at the bottom of the submission form. You can also skip this step and inform us of your non-archival preference after the reviews. Papers accepted to the Findings of NAACL 2025 may also submit non-archival to the workshop, link TBD.

Policies

Accepted and under-review papers are allowed to be submitted to the workshop but will not be included in the proceedings.

No anonymity period will be required for papers submitted to the workshop, per the latest updates to the ACL anonymity policy. However, submissions must still remain fully anonymized.

Info for Participants

To attend the workshop, please register through NAACL 2025 .

Speakers


Mor Geva, Assistant Professor (Senior Lecturer) at Tel Aviv University and a Research Scientist at Google

Mor Geva is an Assistant Professor (Senior Lecturer) at the School of Computer Science and AI at Tel Aviv University and a Research Scientist at Google. Her research focuses on understanding the inner workings of large language models, to increase their transparency and efficiency, control their operation, and improve their reasoning abilities. Mor completed a Ph.D. in Computer Science at Tel Aviv University and was a postdoctoral researcher at Google DeepMind and the Allen Institute for AI. She was nominated as an MIT Rising Star in EECS (2021) and received multiple awards, including Intel's Rising Star Faculty Award (2024), an EMNLP Best Paper Award (2024), an EACL Outstanding Paper Award (2023), and the Dan David Prize for Graduate Students in the field of AI (2020).


Eric Wallace, Research Scientist at OpenAI

Eric Wallace is a research scientist at OpenAI, where he studies the theory and practice of building trustworthy, secure, and private machine learning models. He did his PhD work at UC Berkeley, where he was supported by the Apple Scholars in AI Fellowship and had his research recognized by various awards (EMNLP, PETS). Prior to OpenAI, Eric interned at Google Brain, AI2, and FAIR.


Niloofar Mireshghallah, Post-doctoral Scholar at University of Washington

Niloofar Mireshghallah is a post-doctoral scholar at the Paul G. Allen Center for Computer Science & Engineering at University of Washington. She received her Ph.D. from the CSE department of UC San Diego in 2023. Her research interests are privacy in machine learning, natural language processing and generative AI and law. She is a recipient of the National Center for Women & IT Collegiate award in 2020, a finalist of the Qualcomm Innovation Fellowship in 2021 and a recipient of the 2022 Rising stars in Adversarial ML award and Rising Stars in EECS.

Committee

Organizers

Program Committee

Interested in reviewing for TrustNLP?

If you are interested in reviewing submissions, please fill out this form.

Questions?

Please contact us at trustnlp24naaclworkshop@googlegroups.com.