Recipes, books, car driving: how good are large language models to assist us with chronology and order?
We already noted that Large language models were not good at logic and particularly at Maths.
You can read this previous article containing simple experiments to highlight those issues:
[Nenuphars, gas consumption and crypto stories? Coding is not dead. Yet.]
Today we'll try to experiment with Google Bard the notions of order and chronology in texts.
This idea came from experiments run by my dear friend Joachim.
Large language models are not truly performant polyglots: how to escape the english centric trap?
Many people claim that large language models and particularly ChatGPT can speak and answer in almost all languages like humans.
Those marketing messages take a big shortcut by advertising those tools as efficient polyglots.
The progress in natural language processing is real but some problems and limits do remain on the table.
Are those tools equally efficient in all languages like some of us seem to think?
Isn't English better represented and related prompts receive better outputs?
How are those models trained and how important is the choice and volume of the initial corpus? How do editors fight against hallucinations and cultural biases?