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Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data
Compile what users tell a chatbot into its weights instead of rereading it every prompt.
48.0
F1
MS MARCO answer F1, weights
Beats putting the same ten noisy passages in the prompt
How it works
Frozen models forget
Facts users supply live in the prompt, get reread on every request, then are thrown away after the session.
Generate weights, track belief
A hypernetwork turns each passage into a low-rank weight tweak; a running belief picks which tweak to apply.
Beats prompt and retrieval
On long, noisy evidence, weights beat the prompt, and the trained selector out-routes dense retrieval on four datasets.
From the paper