Follow-up Feedback – Structural Issue in Language Models
In addition to the specific case I previously reported, I’ve noticed a broader and more serious issue that also appears in competing models (including Chinese ones): language models frequently provide completely incorrect information across all fields, not just finance.
The core issue is not simply factual inaccuracy — it's the fact that the model does not clearly acknowledge when it doesn’t know or when it is hallucinating.
This severely undermines trust, because:
The model speaks with confidence, even when wrong.
It fails to disclose its lack of access to real-time or verifiable data.
It prioritizes fluency and completeness over factual accuracy.
This affects high-stakes domains like medicine, law, science, education, and finance.
It’s critical that future versions of the model:
Clearly state when they lack access to real-time or verified data.
Stop simulating confidence when responding with uncertainty or guesswork.
Disclose limitations at the beginning of any answer, especially in sensitive contexts.
This is not just a technical improvement — it’s a matter of ethical responsibility. Users are being misled by confident-sounding responses that are factually wrong. That needs to change.
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