https://elanbarenholtz.github.io/#evidence
This guy seems to be on the right track. The Morphosyntax Experiment If syntax is distributional structure over high-leverage tokens, then function words (THE, WAS, TO) and morphology (-ING, -ED, -LY) should constrain predictions even when surrounded by nonsense. We tested this by measuring next-token entropy in language models across four conditions: Real Sentences: "The teacher was explaining the concept clearly" Jabberwocky: "The blicket was florping the daxen grentily" (function words + morphology intact) Stripped: "Ke blicket nar florp ke daxen grenti" (all nonwords, no morphology) Random Nonwords: Completely unstructured *Results:* Sentences (7.45 bits) < Jabberwocky (8.04) < Stripped (9.07) < Random (9.27) Morphosyntax alone reduces entropy by ~1 bit (p < 0.0001, d = -1.75). Function words and morphological markers constrain prediction independently of semantic content — exactly as predicted by the distributional account. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T33aa28f274d02422-Ma259f1137fb92f305bbbb1c7 Delivery options: https://agi.topicbox.com/groups/agi/subscription
