_Elena_Uve_Alamy.jpg?disable=upscale&width=1200&height=630&fit=crop&w=400&resize=400,250&ssl=1 "Adapt Third-Party API Security to Three Specific Use Cases")
Models
Hugging Face shows how test-time scaling helps small language models punch above their weight
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In a new case study, Hugging Face researchers have demonstrated how small language models (SLMs) can be configured to outperform much larger models. Their findings show that a Llama 3 model with 3B parameters can outperform…
FACTS Grounding: A new benchmark for evaluating the factuality of large language models
Responsibility & Safety Published 17 December 2024 Authors FACTS team Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations Large language models (LLMs) are transforming how we access information, yet their grip on factual accuracy remains imperfect. They can “hallucinate”…
Ecologists find computer vision models’ blind spots in retrieving wildlife images
Try taking a picture of each of North America’s roughly 11,000 tree species, and you’ll have a mere fraction of the millions of photos within nature image datasets. These massive collections of snapshots — ranging from butterflies to humpback whales — are a great research tool for ecologists because they provide evidence of organisms’ unique behaviors, rare conditions,…
Virtual Personas for Language Models via an Anthology of Backstories
We introduce Anthology, a method for conditioning LLMs to representative, consistent, and diverse virtual personas by generating and utilizing naturalistic backstories with rich details of individual values and experience. What does it mean for large language models (LLMs) to be trained on massive text corpora, collectively produced by millions and billions of distinctive human authors?…
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