Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/333536 
Autor:innen: 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Discussion Papers of the Max Planck Institute for Research on Collective Goods No. 2025/12
Verlag: 
Max Planck Institute for Research on Collective Goods, Bonn
Zusammenfassung: 
This Comment shows how large language models (LLMs) can help courts discern the "ordinary meaning" of statutory terms. Instead of relying on expert-heavy corpus-linguistic techniques (Gries 2025), the author simulates a human survey with GPT-4o. Demographically realistic AI agents replicate the 2,835 participants in Tobia's 2020 study on vehicle and yield response distributions with no statistically significant difference from the human data (Kolmogorov-Smirnov p = 0.915). The paper addresses concerns about hallucinations, reproducibility, data leakage, and explainability, and introduces the locked-prompt "Ordinary Meaning Bot," arguing that LLM-based survey simulation is a practical, accurate alternative to dictionaries, intuition, or complex corpus analysis.
Schlagwörter: 
Ordinary meaning
large language models
prompt engineering
human survey simulation
alignment
JEL: 
K1
Z0
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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