This paper introduces a systematic prompt engineering approach to automatically extract Agent-based Model (ABM) information from conceptual documents using LLMs and Question-Answering techniques.
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https://arxiv.org/abs/2412.04056
Original Problem 🤔:
Implementing ABM (Agent-based Model) simulations requires extracting complex information from conceptual models, which is challenging due to diverse skill requirements and extensive documentation. Manual extraction is time-consuming and error-prone.
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Solution in this Paper 🛠:
→ The paper presents a structured set of 9 prompts designed to extract ABM information systematically
→ Each prompt targets specific components: model purpose, agent sets, environment details, and execution parameters
→ The extracted information is formatted in JSON, making it readable for both humans and machines
→ The prompts are carefully engineered to avoid nested structures and maintain high accuracy
→ A QA-based approach is chosen over direct code generation to ensure reliable information extraction
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Key Insights 💡:
→ Breaking down complex prompts into smaller, focused ones improves extraction accuracy
→ JSON formatting enables automated code generation while maintaining human readability
→ Standardized instructions across prompts reduce redundancy and improve consistency
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Results 📊:
→ Successfully extracts model purpose, agent behaviors, and execution parameters from conceptual documents
→ Maintains high accuracy by avoiding nested prompts and complex structures
→ Enables automated transformation of conceptual models into implementable code
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