Its common to read about LLMs being assessed against standardised human tests, but specialised benchmarks are the gold standard when it comes to evaluating their capabilities. In this first of two articles about LLM benchmarks, we will explore why LLM benchmarks are important, and delve into different types and examples of LLM benchmarks.

They may be small but when it comes to their societal and economic implications, Small Language Models (SLMs) are likely to punch far above their weight. SLMs are cheap and easier to train, set-up, and run than their larger counterparts, and can be designed to excel at specific tasks, making them a perfect tool for businesses and organisations of all sizes.

Back in 2016, DeepMind’s AlphaGo displayed signs of genuine creativity when it defeated one of the world’s best Go players. More recently, OpenAI’s secret Q* model reportedly takes the technology behind AlphaGo forward to enable optimal and efficient decision-making, and could be a hint as to the capabilities of next generation AI models.

It’s tough enough trying to get humans to understand each other, much less emotionless machines! Have you then ever wondered how researches have been able to help ChatGPT and other AI models to understand and interpret the complexities and context in human languages, and to respond in a contextually-aware manner? Read on to learn more!