A recent study has found that LLMs are worse at giving accurate, truthful answer
On April 8th, a study began making its way through the AI research conversation carrying a finding that should make anyone who has praised LLMs as great equalisers sit with some discomfort. The claim is not that language models hallucinate randomly, which is the version of the problem we have largely made peace with. The claim is more pointed than that: models are specifically, measurably less accurate and less truthful when the person asking has lower English proficiency or less formal education. Not uniformly unreliable. Directionally unreliable — pointing away from the people who can least afford bad information and have the fewest tools to detect it. That is where the story stood as of early April, a single signal, a single study, waiting for replication or refutation.
If confirmed, here is what this means. The democratisation argument — that AI tools give everyone access to the kind of patient, knowledgeable guidance previously reserved for those who could pay for lawyers, doctors, and tutors — does not just weaken. It inverts. The people most likely to trust an LLM without cross-referencing it, because they lack the educational scaffolding to know when to be sceptical, are precisely the people getting the least reliable answers. That is not a bug at the margins. It is a structural feature of how these systems are trained, almost certainly on corpora that skew toward formal, educated, native-English writing. Second-order effects ripple outward: regulators building AI access into public services, NGOs deploying chatbots for health or legal information in underserved communities, educators positioning AI as a levelling tool for students who struggle with the curriculum. Every one of those use cases looks different if the underlying reliability gradient runs in this direction.
Watch for independent replication of the core finding, and for any response from major model developers addressing whether proficiency-correlated accuracy gaps appear in their own internal evaluations.
<!-- SC_OFF --><div class="md"><p>Study link: <a href="https://ojs.aaai.org/index.php/AAAI/article/view/41259">https://ojs.aaai.org/index.php/AAAI/article/view/41259</a></p>
<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1srx4i1/new_llm_position_bias_benchmark_does_an_llm_keep/"> <img alt="New LLM Position Bias Benchmark: does
<!-- SC_OFF --><div class="md"><p>As LLMs become more prevalent in our society, much of their own training data is AI created.</p> </div><!-- SC_ON -->   submitted by   <a
<!-- SC_OFF --><div class="md"><p>Something I keep noticing is that LLMs can give really strong, coherent answers in a single prompt, but as soon as the conversation goes on for a
<!-- SC_OFF --><div class="md"><p>I have seen a lot of models have gotten dumber posts and am just curious what really happens. </p> <p>Do models get dumber or You got used to the
<!-- SC_OFF --><div class="md"><p>Says that most machine translation tools (even Google Translate) and LLMs have a bias for languages that are not English. If you try to learn
<!-- SC_OFF --><div class="md"><p>not custom instructions. everyone knows custom instructions.</p> <p>something inside custom instructions that almost nobody uses correctly.</p>
  submitted by   <a href="https://www.reddit.com/user/SnoozeDoggyDog"> /u/SnoozeDoggyDog </a> <br /> <span><a
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