#language-models

#language-models

Researchers identified 454 overused terms from AI language models, revealing that 13.5 to 40 percent of biomedical article abstracts were likely generated or assisted by AI.

The results for phi-3-mini on standard open-source benchmarks measure the model's reasoning ability, comparing it to phi-2 and several other notable models.

QDyLoRA offers an efficient and effective technique for LoRA-based fine-tuning LLMs on downstream tasks, eliminating the need for tuning multiple models for optimal rank.

The AI industry's quest for high-quality training data has led companies like Anthropic to explore controversial practices in acquiring books for their models.

In sequence labeling tasks, traditional metrics like the F1 score are insufficient. Our study introduces a modified approach to better assess model performance in identifying praise.

"While commercial LLMs incorporate safety mechanisms to block harmful outputs, these safeguards are increasingly proving insufficient. A critical vulnerability lies in jailbreaking..."

As a conversation designer, it's important to understand some of the techniques used to optimize large language models (LLMs).

The 'Best of Summer' inserts in the Chicago Sun-Times and Philadelphia Inquirer included factual inaccuracies signifying the impact of AI in journalism.

Meta's generative AI developer conference, LlamaCon, was to unveil the 'Behemoth' model, but due to development struggles, the release has been postponed, with concerns about its capabilities.

Microsoft's BitNet b1.58 2B4T, the first natively trained with 1-bit weights, achieves performance akin to full-precision models while drastically reducing needs for computation and resources.

Scaling AI models from local tests to production involves managing significant resource requirements, with models needing up to 40GB of GPU memory for handling multiple requests efficiently.