A recent study from the Massachusetts Institute of Technology examines how large language models (LLMs) respond to systematic disruptions in prompt design when solving math word problems. The findings indicate that even minor additions of irrelevant context can significantly degrade performance.<br /> The article Irrelevant input causes LLM failures — what it means for writing effective prompts appeared first on THE DECODER. [...]
This weekend, Andrej Karpathy, the former director of AI at Tesla and a founding member of OpenAI, decided he wanted to read a book. But he did not want to read it alone. He wanted to read it accompan [...]
Agents are the trendiest topic in AI today — and with good reason. Taking gen AI out of the protected sandbox of the chat interface and allowing it to act directly on the world represents a leap for [...]
AI vibe coders have yet another reason to thank Andrej Karpathy, the coiner of the term. The former Director of AI at Tesla and co-founder of OpenAI, now running his own independent AI project, recent [...]
Unrelenting, persistent attacks on frontier models make them fail, with the patterns of failure varying by model and developer. Red teaming shows that it’s not the sophisticated, complex attacks tha [...]
Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]
When OpenAI went down in December, one of TrueFoundry’s customers faced a crisis that had nothing to do with chatbots or content generation. The company uses large language models to help refill pre [...]