However, many practical applications based on deep networks require real-time processing with minimal latency, which is challenging due to the high computational and storage demands.

As part of a trend loosely called'sovereign AI', governments around the world are developing their own AI technologies As part of a trend loosely called'sovereign AI', governments around the world are developing their own AI technologies Governments are spending billions on their own'sovereign' AI technologies - is it a big waste of money? The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. In Malaysia, ILMUchat, built by a local construction conglomerate, boasts that it "knows which Georgetown you're referring to" - that is, the capital of Penang and not the private university in the US. Meanwhile, Switzerland's Apertus, unveiled in September, understands when to use the Swiss German "ss" and not the German-language character "ß".

Distributed representations provide a vector space that captures meaningful relationships between data instances.

Figure 1: A conditional distribution defined by a conditional generative model.

However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats. Therefore, the construction of an appropriate prompt is essential for improving the performance of in-context learning. In this paper, we revisit this problem from the view of predictive bias. Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes.