Prompt Engineering Guidance for Conceptual Agent-based Model Extraction using Large Language Models
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
This document contains detailed information about the prompts used in the experimental process discussed in the paper "Toward Automating Agent-based Model Generation: A Benchmark for Model Extraction using Question-Answering Techniques". The paper aims to utilize Question-answering (QA) models to extract the necessary information to implement Agent-based Modeling (ABM) from conceptual models. It presents the extracted information in formats that can be read by both humans and computers (i.e., JavaScript Object Notation (JSON)), enabling manual use by humans and auto-code generation by Large Language Models (LLM).
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 0 of its 7 references that are in the map. First posted on arXiv 5 Dec 2024.
Builds on
0 of 7 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle
- 2026Large Language Models as Supervised Extraction Assistants: Lowering the Barrier to Documentation Standard Adoption in Agent-Based Modelling
- 2025A Survey of Large Language Model Agents for Question Answering
- 2025Towards more Contextual Agents: An extractor-Generator Optimization Framework
- 2024Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization
- 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
- 2024The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models
- 2024Prompt Design and Engineering: Introduction and Advanced Methods