Develop a Low-Resource Language Data Generation Crew
Challenge of building LLMs for low-resource languages, this challenge focuses on creating an intelligent agent crew to significantly accelerate and enhance the creation of high-quality, culturally-relevant datasets. The lack of robust training data is a major hurdle for these languages, and traditional methods are slow and costly. Your task is to build a multi-agent system that collaborates to generate synthetic text, translate, and perform linguistic and cultural validation for a specified low-resource language. The system will employ Gemini 3 for advanced generative capabilities and cross-lingual understanding, alongside specialized agents for validation. Crucially, the agents will communicate via an A2A Protocol, ensuring efficient hand-offs and feedback loops, with human-in-the-loop checkpoints for final verification.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a role-based multi-agent system using CrewAI, Gemini 3, and A2A Protocol for synthetic data generation and validation for low-resource languages.
How work is evaluated
Evaluation will assess the quality, diversity, and cultural appropriateness of the synthetically generated dataset, and the efficiency of the agent collaboration.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Integrate human-in-the-loop feedback mechanisms for linguistic and cultural validation of generated data.
Utilize DSPy for declarative programming and optimization of agent prompts and logic for data quality.
Implement agent-to-agent (A2A) protocol for seamless communication and task orchestration between agents.
Leverage Gemini 2.5 Pro for advanced text generation, translation, and cross-lingual reasoning.
Design a role-based agent crew using CrewAI for synthetic data generation and validation in a low-resource language.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
Evaluation will assess the quality, diversity, and cultural appropriateness of the synthetically generated dataset, and the efficiency of the agent collaboration.
Challenge input
{ "language_code": "string", "topics": ["string"], "validation_criteria": "string" }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ "generated_data": [ { "id": "string", "text": "string", "original_topic": "string", "validation_status": "string", "validator_notes": "string" }...
- System generates at least 45 unique snippets for the given task.
- At least 80% of generated snippets are marked as 'Validated' by the agent crew (simulated HIL).
- Overall Quality Score target: 0.85
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- System generates at least 45 unique snippets for the given task.
- At least 80% of generated snippets are marked as 'Validated' by the agent crew (simulated HIL).
Metrics
- Overall Quality Score · Target 0.85
- A2A Handoff Efficiency · Target 0.95
Workflow tasks
- Task 1
Input: { "language_code": "string", "topics": ["string"], "validation_criteria": "string" }
Output: { "generated_data": [ { "id": "string", "text": "string", "original_topic": "string", "validation_status": "string", "validator_notes": "string" } ], "overall_quality_score": "float" }
Execution harness: Python
- Task 2
Input: { "task_description": "string", "expected_agent_roles": ["string"] }
Output: { "communication_log": [ { "sender": "string", "receiver": "string", "message": "string", "timestamp": "string", "task_status_update": "string" } ], "total_messages": "int", "successful_handoffs": "int" }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.