NextBigFuture

Tokens and Tokenization are an Important for Fundamental LLM Understanding

Tokens are the fundamental units that LLMs process. Instead of working with raw text (characters or whole words), LLMs convert input text into a sequence of numeric IDs called tokens using a ...
VentureBeat

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...
No Jitter

Learning to Live With Your UCaaS LLM, Part 1

(Author’s note: this article in its entirety was written without the help of generative AI (Gen AI) in any way, nor was AI used to generate any graphics, either.) Leveraging the large language models ...
TechCrunch

Instead of fine-tuning an LLM as a first approach, try prompt architecting instead

Amid the generative AI eruption, innovation directors are bolstering their business’ IT department in pursuit of customized chatbots or LLMs. They want ChatGPT but with domain-specific information ...
Geeky Gadgets

How to easily fine-tune Llama 2 LLM models in just 5 minutes

If you are interested in learning more about how to fine-tune large language models such as Llama 2 created by Meta. You are sure to enjoy this quick video and tutorial created by Matthew Berman on ...
VentureBeat

Nvidia researchers boost LLMs reasoning skills by getting them to 'think' during pre-training

Researchers at Nvidia have developed a new technique that flips the script on how large language models (LLMs) learn to reason. The method, called reinforcement learning pre-training (RLP), integrates ...