GPT-4 System Card

GPT-4 System Card

OpenAI

Abstract

Large language models (LLMs) are being deployed in many domains of our lives ranging
from browsing, to voice assistants, to coding assistance tools, and have potential for vast societal
impacts.\[1, 2, 3, 4, 5, 6, 7\] This system card analyzes GPT-4, the latest LLM in the GPT family
of models.\[8, 9, 10\] First, we highlight safety challenges presented by the model’s limitations
(e.g., producing convincing text that is subtly false) and capabilities (e.g., increased adeptness
at providing illicit advice, performance in dual-use capabilities, and risky emergent behaviors).
Second, we give a high-level overview of the safety processes OpenAI adopted to prepare GPT-4
for deployment. This spans our work across measurements, model-level changes, product- and
system-level interventions (such as monitoring and policies), and external expert engagement.
Finally, we demonstrate that while our mitigations and processes alter GPT-4’s behavior and
prevent certain kinds of misuses, they are limited and remain brittle in some cases. This points
to the need for anticipatory planning and governance.\[11\]

Content Warning: This document contains content that some may find disturbing or offensive,
including content that is sexual, hateful, or violent in nature.

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1 Introduction

Large language models, also known as LLMs, have become an increasingly prevalent part of our
day-to-day lives, with their use extending to a wide range of domains including web browsing, voice
assistants, and coding assistance tools.\[1, 2, 3, 4\] These models have the potential to significantly
impact society in numerous ways.\[5, 6, 7\] This system card analyzes GPT-4, the latest large language
model in the GPT family of models.\[8, 9, 10\] Since it finished training in August of 2022, we have
been evaluating, adversarially testing, and iteratively improving the model and the system-level
mitigations around it. Our mitigations and processes alter GPT-4’s behavior and prevent certain
kinds of misuses, though they have limitations, pointing to the need for anticipatory planning and
governance\[11\] and further safety research. Our approach to deployment balances minimizing risk
from deployment, enabling positive use cases, and learning from deployment.
GPT models are often trained in two stages. First, they are trained, using a large dataset of text

GPT models are often trained in two stages. First, they are trained, using a large dataset of text
from the Internet, to predict the next word. The models are then fine-tuned with additional data,
using an algorithm called reinforcement learning from human feedback (RLHF), to produce outputs
that are preferred by human labelers.\[10, 12, 13\] Training language models on large text datasets
has given rise to capabilities such as few-shot learning\[10\] and the ability to carry out a wide range
of natural language tasks spanning different domains, including question answering, arithmetic, and
classific

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