Full papers
Papers of up to 8 pages + references reporting on completed, original, and unpublished research; undergo double-blind peer-review; will be published in the ACL Anthology proceedings.
NLP/CORE Unranked
BlackboxNLP 2026
The 9th BlackboxNLP Workshop Special Track: Reproducibility and Reliability in Interpretability Analyses
BlackboxNLP 2026 is the ninth workshop focused on analyzing and interpreting neural networks for NLP, co-located with EMNLP 2026 in Budapest. It brings together researchers from machine learning, psychology, linguistics, and neuroscience to advance the understanding of how NLP models learn representations and make decisions, with a special emphasis on reproducibility and reliability in interpretability methods.
Key deadlines
Verified September 9, 2026Full paper
July 25, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
July 25, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Non-archival extended abstracts
2-page submissions reporting on work in progress or cross-submissions already published elsewhere; non-archival, not included in proceedings; no anonymization required.
Special track papers
Papers of up to 6 pages + references focused on reproducing established interpretability results; undergo double-blind peer-review; will be published in the ACL Anthology proceedings.
Research areas in scope
01
Main Topics
Adapting and applying analysis techniques from other disciplines, such as neuroscience, to analyze high-dimensional vector representations in artificial neural networks.Examining model performance on simplified or formal languages.Proposing architectural modifications to increase models’ interpretability.Testing if interpretable information can be decoded from internal representations.Open-source tools for analysis, visualization, or explanation to democratize access to interpretability techniques in NLP.Meta-evaluation of analysis methods to assess their validity.Understanding how and when language models rely on context information.Analysing the linguistic properties captured by contextualised word representations.Scaling up analysis methods for large language models (LLMs).Mechanistic interpretability, reverse engineering approaches to understanding particular properties of neural models.Evaluation of techniques for steering LLM output behavior.Uncovering the reasoning processes of LLMs.Understanding under the hood of memorization in LLMs.Insights into LLM Failures.Translating interpretability insights into practical solutions to address key challenges in NLP.Opinion pieces about the state of interpretability in NLP.
Policies worth checking twice
- Dual submissions are allowed if the other venue also allows them; if accepted to BlackboxNLP’s archival track, the submission to the other venue must be withdrawn.
- Papers posted to preprint servers such as arXiv can be submitted without restrictions on when they were posted.
- Archival papers (full and special track) must be fully anonymized for double-blind review.
- Non-archival extended abstracts do not require anonymization.
- Accepted archival papers may use one extra page (total 9 for full papers, 7 for special track) to address reviewer comments, plus unlimited references and appendices.
- Broader Impacts/Ethics and Limitations sections are optional and may be included on an additional page.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026