Semantic Anchors

Forecasting With a Spine: How Semantic Anchors Might Fix Time-Series LLMs Forecasting looks simple until the spreadsheet starts moving. A retailer wants next month’s demand. A grid operator wants tomorrow’s load. A finance team wants exchange-rate exposure. In each case, the raw material is not language. It is a jagged sequence of numbers: trend, seasonality, shocks, noise, reporting quirks, holiday distortions, and the occasional data pipeline accident wearing a fake moustache. ...

A label looks harmless until you ask it to lie. Tell a model that a glowing movie review should be labeled POS, and few-shot prompting behaves like a useful intern: it studies the examples, picks up the pattern, and usually gets better. Tell the same model that a glowing review should now be labeled NEG, and the intern becomes less useful. It does not smoothly learn your private code. It does not politely invert its semantic universe. It mostly produces a muddle. ...