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Autores principales: Holtzman, Ari, Tan, Chenhao
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2507.00163
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author Holtzman, Ari
Tan, Chenhao
author_facet Holtzman, Ari
Tan, Chenhao
contents Prompting is the primary method by which we study and control large language models. It is also one of the most powerful: nearly every major capability attributed to LLMs-few-shot learning, chain-of-thought, constitutional AI-was first unlocked through prompting. Yet prompting is rarely treated as science and is frequently frowned upon as alchemy. We argue that this is a category error. If we treat LLMs as a new kind of complex and opaque organism that is trained rather than programmed, then prompting is not a workaround: it is behavioral science. Mechanistic interpretability peers into the neural substrate, prompting probes the model in its native interface: language. We contend that prompting is not inferior, but rather a key component in the science of LLMs.
format Preprint
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publishDate 2025
record_format arxiv
spellingShingle Prompting as Scientific Inquiry
Holtzman, Ari
Tan, Chenhao
Computation and Language
Prompting is the primary method by which we study and control large language models. It is also one of the most powerful: nearly every major capability attributed to LLMs-few-shot learning, chain-of-thought, constitutional AI-was first unlocked through prompting. Yet prompting is rarely treated as science and is frequently frowned upon as alchemy. We argue that this is a category error. If we treat LLMs as a new kind of complex and opaque organism that is trained rather than programmed, then prompting is not a workaround: it is behavioral science. Mechanistic interpretability peers into the neural substrate, prompting probes the model in its native interface: language. We contend that prompting is not inferior, but rather a key component in the science of LLMs.
title Prompting as Scientific Inquiry
topic Computation and Language
url https://arxiv.org/abs/2507.00163