Why AI Research Writing Needs Evidence Before Generation
Large language models are already very good at producing plausible academic prose. In research writing, that creates a slightly strange problem: the sentence can look finished before the reasoning is.
I ran into this while looking at AI continuations for a chemistry manuscript on metal-halide perovskites.
One paragraph was discussing Mn²⁺ incorporation and photoluminescence. The manuscript was gradually narrowing toward defect-related non-radiative recombination, especially halide-vacancy-related states and local structural changes.
The continuation went somewhere else. It brought in cavity engineering, general defect passivation, and compositional optimisation.
None of those topics was absurd. In another paragraph, they might have worked perfectly well. But this paragraph was trying to do something much narrower.
The model had picked up the subject. It had not picked up the reason this paragraph existed.
That failure interests me more than obvious hallucination, because it is easy to miss. If the continuation had used the wrong material...
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