Date: 2026-10-17 to 2026-10-21
Location: Utrecht, Netherlands
Type: Conference
Website: 2026.inlgmeeting.org
What Is INLG 2026?
The 19th International Conference on Natural Language Generation (INLG 2026) is the premier academic and industry gathering dedicated exclusively to the field of Natural Language Generation (NLG). Scheduled for October 17–21, 2026, in Utrecht, Netherlands, this annual event is organized under the auspices of the Association for Computational Linguistics (ACL) Special Interest Group on Natural Language Generation (SIGGEN). INLG has long served as the central forum for researchers, engineers, and practitioners working on systems that produce human-readable text from structured data, concepts, other text, or visual inputs.
INLG 2026 covers the full spectrum of NLG approaches, including data-to-text (e.g., generating reports from databases), concept-to-text (e.g., producing descriptions from knowledge graphs), text-to-text (e.g., summarization and paraphrasing), and vision-to-text (e.g., image captioning). The conference is particularly timely in 2026, as large language models (LLMs) have fundamentally reshaped the NLG landscape. The program will address how LLMs are being integrated into traditional NLG pipelines, as well as the new challenges they introduce around evaluation, explainability, bias, and fairness. For AI professionals, INLG 2026 represents a critical checkpoint for understanding where the field stands and where it is heading.
Why It Matters for AI Professionals
Natural Language Generation is no longer a niche research area—it is a core capability of modern AI systems. From automated journalism and financial reporting to conversational AI and accessibility tools, NLG powers applications that directly impact business operations and user experience. INLG 2026 offers AI professionals a concentrated look at the latest methodologies for building reliable, controllable, and ethical text generation systems. Attendees will gain insights into state-of-the-art evaluation metrics, techniques for mitigating bias in generated outputs, and methods for making NLG systems more transparent and explainable.
For practitioners working with LLMs, the conference provides a venue to move beyond simple prompt engineering and into deeper architectural and evaluation considerations. Researchers will find rigorous peer-reviewed work on topics such as faithfulness in generation, controlled text production, and multimodal generation. The conference also serves as a networking hub for connecting with leading academics, industry researchers, and tool builders who are shaping the next generation of NLG technology.
What to Expect
INLG 2026 will feature a multi-day program of keynote talks, oral presentations, poster sessions, and workshops. While specific speakers have yet to be announced, the conference typically attracts leading figures from both academia and industry. Key themes for this edition include:
- LLMs for NLG: How large language models are being adapted, fine-tuned, and constrained for reliable text generation tasks.
- Evaluation: New metrics and human evaluation protocols for assessing fluency, faithfulness, and task completion in generated text.
- Explainability: Methods for understanding why an NLG system produces a particular output, including attention analysis and counterfactual explanations.
- Bias and Fairness: Identifying and mitigating harmful stereotypes, representational harms, and demographic biases in training data and generated outputs.
- Multimodal Generation: Advances in vision-to-text and other cross-modal generation tasks.
- Data-to-Text and Concept-to-Text: Structured input pipelines for domains like weather, sports, finance, and healthcare.
Workshops and tutorials will offer hands-on opportunities to explore specific tools and frameworks. The exact schedule and list of accepted papers will be published on the official website as the event approaches.
Who Should Attend
INLG 2026 is designed for a diverse audience within the AI ecosystem. Primary attendees include academic researchers and graduate students specializing in natural language processing, computational linguistics, and machine learning. Industry practitioners—such as NLP engineers, data scientists, and product managers working on text generation features—will find practical insights into deployment challenges and evaluation best practices. Executives and technical leads evaluating NLG solutions for their organizations can benefit from understanding the current capabilities and limitations of the technology. Additionally, professionals focused on AI ethics, fairness, and responsible AI will find dedicated sessions on bias and explainability particularly relevant.
How to Register
Registration for INLG 2026 will open closer to the conference date. Pricing details, including early-bird rates, student discounts, and virtual attendance options, are to be announced. All registration information, including links to the official registration portal, will be published on the conference website. To stay updated and secure your place, visit 2026.inlgmeeting.org and monitor the site for announcements. We recommend checking regularly, as early registration often provides significant savings and ensures access to workshops and social events.
