This only works if the article isn't covering anything very novel. If there isn't a fairly wide base of explanations on the topics within, it struggles and hallucinates.
ChatGPT is the ultimate non-human bullshit artist so far developed. It's scary to think LLMs will improve and will be used by used unethically by students, professionals, state actor boiler rooms, and targeted harassment and manipulation of individuals. For the case of proving original work, it's worth considering recording chain-of-custody with an "anti-cheat" app for data capture and written works will be necessary to prove it was generated by or directed by a human being by recording specific data of creation to establish zero-knowledge, zero-reputation provenance.
The next question becomes: When automation and AI are used in drug discovery and STEM ever more autonomously (they are mostly narrow AI initiated by a human with specific goals today), what's the protocol for distributing credit? It seems plausible in the near future with large piles of computing power to train LLMs against journals, give it access to data, drug/protein/ligand databases, and let it find and screen beneficial candidate molecules for critical need medications such as new classes of antibiotics and antimycotics, and rare degenerative diseases, perhaps including gene edits. Efficient, progressive layers of screening methodology seem important. IIRC, 15 years ago (c. 2007) biomedical informatics people at Stanford (SAIL -> SMI -> BMIR) were using NLP heuristics against the literature for meta-analyses. I assume things progressed by several lightyears hence.
Linguistic transformations are the feature that I trust LLMs the most with. It requires relatively little encoding of knowledge outside of language. Parsing jargon to convey novel ideas is absolutely a doable task for a language model.
Yes. If you can necessarily and sufficiently prompt an LLM with authoritative facts, it can usually generate reasonable copy around it. For example, if you were a PwC or McKinsey consultant tasked with rapidly generating a voluminous, glossy deliverable in need of a consistent, formal style, LLMs offer an initial starting point Easy Button for content generation prior to human editing (perhaps assisted by an on-prem "grammarly" app) and pasting into ye olde templates.
The next question becomes: When automation and AI are used in drug discovery and STEM ever more autonomously (they are mostly narrow AI initiated by a human with specific goals today), what's the protocol for distributing credit? It seems plausible in the near future with large piles of computing power to train LLMs against journals, give it access to data, drug/protein/ligand databases, and let it find and screen beneficial candidate molecules for critical need medications such as new classes of antibiotics and antimycotics, and rare degenerative diseases, perhaps including gene edits. Efficient, progressive layers of screening methodology seem important. IIRC, 15 years ago (c. 2007) biomedical informatics people at Stanford (SAIL -> SMI -> BMIR) were using NLP heuristics against the literature for meta-analyses. I assume things progressed by several lightyears hence.