Concept
Capture of an LLM by selection of the training corpus. The most upstream version of capture in an LLM: choosing what data the model was fitted to (what included, in what proportions, with what filtering) tilts every output regardless of how careful the runtime gate is. A captured corpus produces a captured outer message — every output is decoded by receivers using the very preconditions the corpus installed. Will be unified with related flavors in the forthcoming capture-taxonomy note.
Connections
- Defined in: Capture Taxonomy
- Discussed in: Ch 11 — AI as a New Kind of Node, Ch 12 — Infrastructure for Integration
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