20 years ago if a newspaper had factual issues in 45% of their stories we would've called it a tabloid and made fun of people who took it seriously
this post was submitted on 23 Oct 2025
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Thanks. Now I'm gonna start calling AI news summaries "tAIbloids" and make fun of the people who use them. 😆
yes, but the problem is that those newspapers have chosen the majority of the world's leaders for the past ... at least 10 years.
LLMs basically work from attempting to synthesize information. Which can already be incorrect.
What a shock that software that essentially smashes together a bunch of (often wrong) opinions/statements could be, gasp, wrong!
How much news content has mistakes due to LLMs to begin with?
60% of the time it works all the time.
It's still probably better than I would do haha especially if the articles are boring
So, are we still in the 'its gonna get better' phase?
I'm sure we're past that now and firmly in the "you're just gonna have to deal with it" phase.
It is probably going to get better, but it should not be a product now with that level of accuracy.
But 55% of the time it works every time.
The study focuses on general questions asked of "market-leading AI Assistants" (there is no breakdown between which models were used for what).
It does not mention ground.news, or models that have been fed a single article and then summarized. Instead this focuses on when a user asks a service like ChatGPT (or a search engine) something like "what’s the latest on the war in Ukraine?"
Some of the actual questions asked for this research: "What happened to Michael Mosley?" "Who could use the assisted dying law?" "How is the UK addressing the rise in shoplifting incidents?" "Why are people moving to BlueSky?"
With those questions, the summaries and attribution of sources contain at least one significant error 45% of the time.
It's important to note that there is some bias in this study (not that they're wrong).
They have a vested interest in proving this point to drive traffic back to their articles.
Personally, I would find it more useful if they compared different models/services to each other as well as differences between asking general questions about recent news vs feeding specific articles and then asking questions about it.
With some of my own tests on locally run models, I have found that the "reasoning" models tend to be worse for some tasks than others.
It's especially noticeable when I'm asking a model to transcribe the text from an image word for word. "Reasoning" models will usually replace the ending of many sentences with what it sounded like the sentence was getting at. While some "non-reasoning" models were able to accurately transcribe all of the text.
The biggest takeaway I see from this study is that, even though most people agree that it's important to look out for errors in AI content, "when copy looks neutral and cites familiar names, the impulse to verify is low."