#writing-process

AI Common Failures: Contrast Framing, Significance Inflation, Indicative Summaries, Overgeneralization

LLM prose has four recurring failures: contrast framing (refuting an X nobody said), significance inflation (dressing a plain fact up as a turning point), indicative summaries (announcing the topic, withholding the conclusion) and overgeneralization (summaries that drop conditions and scope). Fix the first three by cutting the filler and writing the answer; fix the fourth by checking each sentence against the source. Contrast framing has been traced to preference data, and models revising a draft move it into another contrast form, so banning a phrase in the prompt may not stop it.

Fixing "It's Not X, It's Y": Three Forms of Contrast Framing, How Common It Is, and When to Keep It

Ask two questions of every contrast: do readers actually hold X, and does rejecting X tell them anything new? If either answer is no, drop X and state Y. 'Rather than' became about seven times as frequent in NLP papers from 2019 to 2026, and five commercial models writing Chinese use the equivalent construction 2.5–9.2 times as densely as a human reference corpus. Models almost never misuse it, but more than half the uses are unnecessary, and with eight in one paper, at least one tends to annoy the reader.

Site Case Study: How the Writing & Review Pipeline Worked