In recent years, the evolution of large language models has skyrocketed. BERT became one of the most popular and efficient models allowing to solve a wide range of NLP tasks with high accuracy. After…
As large language models (LLMs) become commercial commodities, there is a growing focus on making them smaller and faster. Small models, which can be squeezed onto fewer chips, are much less expensive to train and to run—some can even run on a laptop or smartphone. Developers have been ...
As large language models (LLMs) have entered the common vernacular, people have discovered how to use apps that access them. Modern AI tools can generate, create, summarize, translate, classify and even converse. Tools in the generative AI domain allow us to generate responses to prompts ...
Open-source small language models (SLMs) can provide conversational responses that are similar to resource-intensive, proprietary large language models (LLMs) such as OpenAI's ChatGPT, but at a lower cost, researchers at the University of Michigan have found. Their findings arepublishedon thearX...
The most popular and widely used models today are known as large language models (LLMs). LLMs can be massive. The technology is tied to large, diverse troves of information and the models contain billions— sometimes even trillions — of parameters (or variables) that can make them bot...
Google LLC is advancing its efforts in open-source artificial intelligence with three new additions to its Gemma 2 family of large language models, which it said are notably “smaller, safer and more transparent” than many of their peers. ...
we improve the MiniVLM pre-training by adding7MOpen Images data, which are pseudo-labeled by a state-of-the-art captioning model. We also pre-train with high-quality image tags obtained from a strong tagging model to enhance cross-modality alignment. The large models are used offline wi...
But what if the best way to manage large datasets is to make them smaller? There’s currently a move afoot to utilize smaller datasets when developing large language models (LLMs) to promote better feature representation and enhance model generalization. Curated smaller datasets can represent releva...
Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less th...
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora. We propose a language model based o... E Shareghi,M Petri,G Haffari,... 被引量: 3发...