The Guardian -“…Looking into the sort of evidence that large language models (LLMs, the engines on which chatbots are built) find most convincing, three computer science researchers from the University of California, Berkeley, found current chatbots overrely on the superficial relevance of information. They tend to prioritise text that includes pertinent technical language or is stuffed with related keywords, while ignoring other features we would usually use to assess trustworthiness, such as the inclusion of scientific references or objective language free of personal bias…an internet dominated by pliant chatbots throws up issues of a more existential kind. Ask a search engine a question, and it will return a long list of webpages. Most users will pick from the top few, but even those websites towards the bottom of the results will net some traffic. Chatbots, by contrast, only mention the four or five websites from which they crib their information as references to the side. That casts a big spotlight on the lucky few that are selected and leaves every other website that isn’t picked practically invisible, plummeting their traffic…

For readers, too, the presentation of chatbot responses makes them only more fertile for manipulation. “If LLMs give a direct answer to a question, then most people may not even look at what the underlying sources are,” says Wan. Such thinking points to a broader worry that has been termed the “dilemma of the direct answer”: if a person is given a single answer to a question and offered no alternatives to consider, will they diligently look for other views to weigh the initial answer against? Probably not. More likely, they’ll accept it as given and move on, blind to the nuances, debates and differing perspectives that may surround it…”