GPTs are GPTs: An early look at the labor market impact potential of large language models

Understanding the capabilities, limitations, and societal impact of large language models

Economic impacts research at OpenAI

Call for expressions of interest to study the economic impacts of large language models.

GPT-2: 6-month follow-up

We’re releasing the 774 million parameter GPT-2 language model after the release of our small 124M model in February, staged release of our medium 355M model in May, and subsequent research with partners and the AI community into the model’s potential for misuse and societal benefit. We’re also releasing an open-source legal agreement to make it easier for organizations to initiate model-sharing partnerships with each other, and are publishing a technical report about our experience in coordinating with the wider AI research community on publication norms.

"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

arXiv:2308. 05201v4 Announce Type: replace Abstract: Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics.

Economic Evaluations of Language Models

arXiv:2607. 19375v1 Announce Type: cross Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task.

GPT-2: 1.5B release

As the final model release of GPT-2’s staged release, we’re releasing the largest version (1. 5B parameters) of GPT-2 along with code and model weights to facilitate detection of outputs of GPT-2 models.

Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs

arXiv:2607. 09121v1 Announce Type: cross Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.

WebGPT: Improving the factual accuracy of language models through web browsing

We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.

Fine-tuning GPT-2 from human preferences

We’ve fine-tuned the 774M parameter GPT-2 language model using human feedback for various tasks, successfully matching the preferences of the external human labelers, though those preferences did not always match our own. Specifically, for summarization tasks the labelers preferred sentences copied wholesale from the input (we’d only asked them to ensure accuracy), so our models learned to copy.

When transformers learn "impossible" languages, what do they learn?

arXiv:2606. 30815v1 Announce Type: cross Abstract: Recent work suggests that transformer language models show a bias towards human languages over unnatural ("impossible") languages argued to be unacquirable by humans.

Introducing GPT-5.4

Introducing GPT-5. 4, OpenAI’s most most capable and efficient frontier model for professional work, with state-of-the-art coding, computer use, tool search, and 1M-token context.

On the Smallness of the Large Language Models Scaling Exponents

arXiv:2606. 24504v1 Announce Type: new Abstract: We discuss reasons why the scaling exponents of current Large Language Models (LLMs) applications are indicating an unsustainable regime in terms of energy resources.