As the excitement about the immense potential of large language models (LLMs) dies down, now comes the hard work of ironing out the things they don’t do well. The word “hallucination” is the most ...
Training AI models is a whole lot faster in 2023, according to the results from the MLPerf Training 3.1 benchmark released today. The pace of innovation in the generative AI space is breathtaking to ...
A new technical paper titled “MLP-Offload: Multi-Level, Multi-Path Offloading for LLM Pre-training to Break the GPU Memory Wall” was published by researchers at Argonne National Laboratory and ...
Running large language models at the enterprise level often means sending prompts and data to a managed service in the cloud, much like with consumer use cases. This has worked in the past because ...
On the surface, it seems obvious that training an LLM with “high quality” data will lead to better performance than feeding it any old “low quality” junk you can find. Now, a group of researchers is ...
Hosted on MSN
Anthropic study reveals it's actually even easier to poison LLM training data than first thought
Claude-creator Anthropic has found that it's actually easier to 'poison' Large Language Models than previously thought. In a recent blog post, Anthropic explains that as few as "250 malicious ...
Contrary to long-held beliefs that attacking or contaminating large language models (LLMs) requires enormous volumes of malicious data, new research from AI startup Anthropic, conducted in ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results