The limitations of language: AI models still lag behind humans in simple text comprehension tests
An international research team led by the URV has analyzed the capabilities of seven artificial intelligence (AI) models in understanding language and compared them with those of humans.
Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these dimensions before being reliably used…
A new study led by researchers at the University of Oxford and the Allen Institute for AI (Ai2) has found that large language models (LLMs)—the AI systems behind chatbots like ChatGPT—generalize language patterns in a surprisingly human-like way: through analogy, rather than strict grammatical rules.
Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs integrate data inputs across modalities in a central hub that processes data in an input-type-agnostic fashion.