DeepMind developed SAFE, an AI agent to fact-check LLMs
Researchers from DeepMind and Stanford University developed an AI agent that fact-checks LLMs and enables benchmarking of AI model factuality.…
Personalized LLMs are becoming more persuasive than humans
A team of researchers found that once a large language model (LLM) is personalized with a person’s demographic information, it…
Quiet-STaR teaches language models to think before they speak
Researchers from Stanford University and Notbad AI developed Quiet-STaR, a technique that trains a language model (LM) to reason internally…
WMDP measures and reduces LLM malicious use with unlearning
Researchers released a benchmark to measure whether an LLM contains potentially hazardous knowledge and a novel technique for unlearning dangerous…
The dissonance of generative AI’s growing distrust and rising usership
The rapid rise of generative AI has captivated the world, but as the technology advances at an unprecedented pace, a…
Researchers jailbreak LLMs by using ASCII art in prompts
Researchers developed a jailbreak attack called ArtPrompt, which uses ASCII art to bypass an LLM’s guardrails. If you remember a…
African American English (AAE) influences LLMs towards discrimination
Bias has always been a problem in AI, but a new study shows that it’s covertly integrated into language models…
LLMs produce more inaccurate and biased outputs with longer inputs
Despite rapid advancements in LLMs, our understanding of how these models cope with longer inputs remains poor. Mosh Levy, Alon…
Meta, UCSD introduce ToolVerifier to improve LLM tool calls
Researchers from Meta and the University of California San Diego (UCSD) developed ToolVerifier, a method that improves how LLMs call…
Generative AI systems, hallucinations, and mounting technical debt
As AI systems like large language models (LLMs) grow in size and complexity, researchers are uncovering intriguing fundamental limitations. Recent…