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Welcome to GenSIE 2026

GenSIE (General-purpose Schema-guided Information Extraction) is a shared task at IberLEF 2026 focusing on the ability of systems to extract nested, structured information (JSON) from general-domain Spanish texts.

Read the full task description, including score metrics and detailed constraints.

The task challenges participants to use Small Language Models (SLMs) and inference-time techniques to handle Zero-Shot Schemas—where the extraction target is defined dynamically at runtime.

  • Zero-Shot Schema

    Extract data using schemas seen only at inference time. No fixed ontology.

  • General Domain

    From legal contracts to medical reports and news.

  • Inference-Time Focus

    Focus on prompting, RAG, and constrained decoding. No massive fine-tuning.

  • Structured Output

    Strict adherence to JSON Schema and complex semantic constraints.

Resources

Explore our technical documentation to get started:

Schedule

Date Event
✅ March 06, 2026 🚀 Starter Kit Released (View Guide)
April 01, 2026 📂 Full Development Set (Remaining 170 examples)
May 08, 2026 🛑 Submission Deadline (Docker containers)
May 09, 2026 🔓 Test Set Release (For local error analysis)
May 09–30, 2026 ⚙️ Evaluation Period (Hosted execution)
May 31, 2026 🏆 Results Announcement
June 07, 2026 📝 Paper Submission Deadline
Sept 22, 2026 🎤 IberLEF Workshop (León, Spain)

News & Updates

  • Jan 26, 2026: Website launched.
  • March 01, 2026: We've had some delays with the preparation of the starter-kit which forced to push the date back to March 09 at the latest.

Motivation

The rise of Agentic Workflows has created a massive demand for systems that can communicate via structured protocols. To identify user intent, invoke external tools, or exchange information, an AI must output rigid, error-free structured data.

While massive proprietary models (like GPT-5) solve this through scale, GenSIE targets the innovation gap in Small Language Models (<14B). We aim to prove that with clever engineering (Chain-of-Thought, ReAct, Constrained Decoding), commodity hardware can perform complex structured extraction reliably.

Furthermore, we aim to prioritize efficiency and sustainability to ensure that high-performance extraction pipelines remain deployable in real-world scenarios. By focusing on models that run on consumer-grade hardware, we promote sustainable AI and cost-effective solutions that are accessible to smaller research groups and industry practitioners.

Organizing Committee

The GenSIE task is organized by a consortium between the Research Group on Artificial Intelligence and Data Science (GIA-UH) at the University of Havana and the Research Group in Natural Language Processing and Information Systems (GPLSI) at the University of Alicante.

This team brings together expertise in both Computer Science (Generative AI, Large Language Models) and Linguistics (Corpus Annotation, Semantic Evaluation).

Members

Name Affiliation Role
Yudivian Almeida Cruz University of Havana PhD, Professor
Suilan Estévez Velarde University of Havana PhD, Professor
Alejandro Piad Morffis University of Havana PhD, Professor
Isabel Espinosa Zaragoza University of Alicante PhD, Assistant Professor
María Miró Maestre University of Alicante PhD, Postdoc Researcher
Lucía Sevilla Requena University of Alicante PhD Student, Assoc. Prof.
Alba Pérez Montero University of Alicante PhD Student
Ernesto Estevanell Valladares University of Havana PhD Student

Contact

For questions regarding the task, dataset, or evaluation, please contact the corresponding author, Alejandro Piad Morffis.