Call for Papers

Workshop for Industrial NLP: NLP Research Towards Safety, Reliability, and Real-World Impact

17 October 2026 · Co-located with INLG 2026 · Half-Day Workshop

We invite submissions to the first Workshop for Industrial NLP, a half-day workshop at INLG 2026 dedicated to natural language generation (NLG) and large language model (LLM) research shaped by real deployment settings.

By industrial NLP, we mean NLP and NLG research driven by the realities of production: deployment constraints, domain-specific risks, user-facing applications, business and public-sector workflows, and the need for measurable reliability beyond benchmark performance.

Recent progress in generative NLP has accelerated industrial adoption across customer support, content generation, finance, cybersecurity, education, healthcare, software engineering, and multilingual communication. Yet deployed systems expose limitations that standard benchmarks often miss: hallucination, inconsistent behavior, prompt sensitivity, safety failures, bias, privacy risks, insufficient domain adaptation, and weak evaluation protocols. These concerns are directly relevant to the INLG community, where the quality of generated language must increasingly be assessed in context: who uses it, what decisions it supports, what harms it may cause, and how reliably it performs under operational constraints.

This workshop brings together researchers and practitioners working on NLG, LLMs, evaluation, safety, fairness, multilingual NLP, and deployed applications, with the goal of supporting industrial systems that are reliable, accountable, robust, and useful in real-world environments.

Objectives

• Create a focused venue for NLG work on safety, reliability, evaluation, and industrial deployment.
• Encourage dialogue between academic researchers and industry practitioners about practical failure modes and realistic evaluation needs.
• Highlight methods for making generated text more controllable, trustworthy, fair, multilingual, and domain-robust.
• Identify open research directions where INLG can contribute to responsible and high-impact NLP systems.

Submission Tracks

We welcome short papers, extended abstracts, position papers, and industrial case studies. To organize review and discussion around the realities of deployed systems, submissions are invited under the following tracks. Tracks are non-exclusive — choose the one that best fits your work, and topics within each are indicative rather than exhaustive.

1. Safety & Security

Content safety, toxicity, and harmful-output mitigation; adversarial robustness, jailbreaks, and prompt injection; guardrails and moderation; privacy, PII leakage, and data protection; security of NLP supply chains and tooling; compliance with safety regulations and standards.

2. Reliability & Fairness

Factuality, hallucination detection, and grounding; calibration, uncertainty, and abstention; consistency, prompt-sensitivity, and robustness to distribution shift; bias, fairness, and equitable behavior across users, dialects, and demographics; trustworthy and accountable generation.

3. Efficiency

Latency-, cost-, and memory-aware NLG; model compression, distillation, quantization, and pruning; efficient inference, caching, and serving at scale; small, on-device, and edge-deployable models; sustainable and energy-aware NLP.

4. Industrial Applications & Deployment

NLG/LLM systems in production across domains — including medicine and healthcare, finance, cybersecurity, law, education, search, assistants, customer support, and content platforms. Deployment constraints (scalability, maintainability, integration); domain adaptation and customization; monitoring, incident analysis, and LLMOps; integration with enterprise and public-sector workflows.

5. Evaluation, Datasets & Benchmarks

Evaluation in context: pipelines, protocols, and metrics that reflect real deployment; human evaluation and LLM-as-judge methods; post-deployment quality assurance and regression testing; new datasets and benchmarks capturing operational conditions, domain shift, and multilingual/real-user settings.

6. NLP for Social Good & Real-World Impact

NLG/LLM systems built for public-interest and high-benefit outcomes: broadening access through low-resource and multilingual NLP; accessibility; applications in healthcare access, education equity, humanitarian and crisis response, public services, and the environment; participatory and human-centered design with affected communities.

7. Negative Impacts & Their Mitigations

Critical analysis of the harms of deployed generative systems — misuse and dual-use, misinformation, labor and economic effects, environmental cost, and over-reliance — paired with concrete, actionable mitigations. Position papers, audits, incident retrospectives, and responsible-disclosure practices are especially welcome.

Submission Types and Format

We solicit:

• Short papers
• Extended abstracts
• Position papers
• Industrial case studies

All submissions to the workshop are non-archival: all accepted contributions are presented at the workshop as talks, but are not published in the ACL Anthology proceedings. This makes the workshop suitable both for mature research and for emerging industrial case studies that cannot yet be published as full archival papers, and it welcomes work in progress as well as work published or under review elsewhere.

Following the INLG 2026 call for papers, submissions should use the official ACL LaTeX/Word style files and follow the ACL Author Guidelines. Short papers may be up to 4 pages of content, with unlimited additional pages for references, ethics/limitations, and supplementary material statements; extended abstracts, position papers, and industrial case studies should likewise not exceed 4 pages of content. Submissions must be anonymised for double-blind review, and should follow the ACL Code of Ethics, discussing any ethical considerations explicitly and including a data availability statement for experimental work.

Submissions are made through OpenReview: Industrial NLP @ INLG 2026 — direct submission. You can also submit via the ARR Commitment track.

We realistically expect 20–45 submissions and plan to accept around 10–20 contributions, with all accepted contributions presented as talks.

Important Dates

All deadlines follow the INLG suggested schedule. Times are end-of-day (anywhere on Earth) unless otherwise noted.

Format

The workshop takes place on 17 October 2026, 13:30–17:00, combining keynotes, paper presentations, and discussion. The program is:

Who Should Submit

The workshop targets the INLG and broader NLG community — especially researchers studying text generation, evaluation, controllability, data-to-text generation, dialogue, summarization, factuality, and human-centered generation. We also welcome participants from adjacent communities in NLP safety, responsible AI, multilingual NLP, fairness, human-computer interaction, information retrieval, and applied machine learning.

A key audience is industrial NLP practitioners who deploy or evaluate generative systems in production.

Organizers

• Yizhi Li — University of Manchester (reasoning and CLI agents)
• Yi Zhou — Cardiff University (responsible AI)
• Jingcheng Niu — TU Darmstadt (mechanistic interpretability)
• Haau-Sing Li — TU Darmstadt and Instituto de Telecomunicações (code generation and test-time compute)
• Joanne Boisson — Cardiff University (social NLP and metaphor)
• Yi Qi — University of Leeds (safety assurance and trustworthy AI)
• Jie Fu — IQuest Research (safety)

Invited Speakers

• Qing Li (University of Groningen) — Keynote 1
• Gerald Penn (University of Toronto) — Keynote 2

Contact

Updates, submission instructions, and the submission portal will be posted on the workshop website. The call will be circulated through SIGGEN, ACL mailing lists, social media, and relevant academic and industry networks.

We look forward to your submissions and to a focused discussion of how NLG research can shape the next generation of deployed NLP systems.

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