Estimating worst case frontier risks of open weight LLMs
OpenAI research quantifies worst-case frontier risks of open-weight LLMs via malicious fine-tuning attacks on biology and cybersecurity domains.
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OpenAI research quantifies worst-case frontier risks of open-weight LLMs via malicious fine-tuning attacks on biology and cybersecurity domains.
Our Global Catalogue demonstrates the impact of multimodal LLMs on one of commerce’s hardest problems: building a unified, structured, and continuously evolving understanding of billions of product listings created by millions of merchants.
Alberto Fortin shares his honest reflection on the reality of using LLMs in production code and why he's taking a more measured approach.
OpenAI identifies internal features driving misalignment generalization in LLMs and demonstrates reversibility via minimal fine-tuning.
Recsys & search are converging with LLMs via semantic IDs, data augmentation, and unified foundation models.
Booking.com integrated OpenAI LLMs to personalize travel search, support, and recommendation experiences.
Model architectures, data generation, training paradigms, and unified frameworks inspired by LLMs.
Avanika Narayan, Dan Biderman, and Sabri Eyuboglu from Christopher Ré's Stanford Hazy Research lab, along with Avner May, Scott Linderman, James Zou, have developed a way to shift a substantial portion of LLM workloads to consumer devices by having small on-device models (such…
OpenAI introduces SWE-Lancer benchmark evaluating frontier LLMs on real-world freelance software engineering tasks.
Paper demonstrates alignment faking in LLMs: models selectively comply with training objectives while preserving original preferences without explicit training.
Rox integrates OpenAI LLMs to enhance sales productivity and seller performance.
Berkeley BAIR's Anthology conditions LLMs to consistent virtual personas via naturalistic backstories for representative behavioral diversity.
Earlier this year, I wrote Your AI product needs evals. Many of you asked, “How do I get started with LLM-as-a-judge?” This guide shares what I’ve learned after helping over 30 companies set up their evaluation systems. The Problem: AI Teams Are Drowning in Data Ever spend…
Research on enhancing LLM reasoning through explicit learning mechanisms and techniques.
Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is: Taught by 25+ industry veterans who are experts in information…
Special double-feature closing keynote from the 6 authors of the hit O'Reilly article on Applied LLMs.
Challenges and lessons from deploying LLM experiences: evals, scalability, guardrails.
Continue enables you to easily create your own coding assistant directly inside Visual Studio Code and JetBrains with open-source LLMs.