How confessions can keep language models honest

Why language models hallucinate

OpenAI’s new research explains why language models hallucinate. The findings show how improved evaluations can enhance AI reliability, honesty, and safety.

Inside our approach to the Model Spec

Learn how OpenAI’s Model Spec serves as a public framework for model behavior, balancing safety, user freedom, and accountability as AI systems advance.

Learning to communicate

In this post we’ll outline new OpenAI research in which agents develop their own language.

Transcoders for Investigating Deception in Language Models

arXiv:2607. 14791v1 Announce Type: new Abstract: Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour.

Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration

arXiv:2608. 08881v1 Announce Type: new Abstract: The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models.

Reasoning models struggle to control their chains of thought, and that’s good

OpenAI introduces CoT-Control and finds reasoning models struggle to control their chains of thought, reinforcing monitorability as an AI safety safeguard.

TruthfulQA: Measuring how models mimic human falsehoods

PRISON: Unmasking the Criminal Potential of Large Language Models

arXiv:2506. 16150v4 Announce Type: replace-cross Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify.

Understanding prompt injections: a frontier security challenge

Prompt injections are a frontier security challenge for AI systems. Learn how these attacks work and how OpenAI is advancing research, training models, and building safeguards for users.

Advancing content provenance for a safer, more transparent AI ecosystem

OpenAI advances AI content provenance with Content Credentials, SynthID, and a verification tool to help people identify and trust AI-generated media.

Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception

arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.

Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk

OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research.