# Prompt Engineering Guidance for Conceptual Agent-based Model Extraction using Large Language Models

Source: [Prompt Engineering Guidance for Conceptual Agent-based Model Extraction using Large Language Models](<https://www.sota2.com/research/paper/68fa760e1e209131cea6b2e2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:46:34.345Z

Authors: Siamak Khatami, Christopher Frantz

Published: 2024-12-05T10:49:02.000Z

## Abstract

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).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.04056>)

[PDF](<https://arxiv.org/pdf/2412.04056.pdf>)