FSL

FSL is defined within this taxonomy exclusively as a Parametric Update intervention under extreme data scarcity. Depending on the implementation, it relies on either 1–5 examples for metric-based support sets (Few Demonstrations) or tens of examples for gradient-based updates (Small Labeled), producing a persistent weight modification (Ad-hoc Permanent, Scheduled Permanent) ^{[185, 201]}. This strict boundary resolves the taxonomy's most consequential terminological ambiguity: colloquial ``few-shot'' usage conflates two mechanistically and operationally distinct operations. The parametric form produces a versioned model artifact that has undergone a parameter-modifying training event and therefore demands formal change-control tracking and baseline regression testing. The non-parametric, prompting-based form shares Few Demonstrations but operates via Context Injection, producing only a Session-Ephemeral behavioral shift that leaves the underlying model artifact unmodified; this is reassigned to ICL. The D4 (Persistence) divergence is the taxonomic anchor: FSL creates a new versioned artifact requiring software change-control; ICL creates no artifact whatsoever. FSL's DL, FM ceiling mirrors DA's: at the LLM tier, ICL achieves rapid few-shot task specialization through context conditioning alone, making formal FSL training pipelines architecturally redundant for pure few-shot tasks at that scale. FSL necessitates version-control tracking of the resulting weight checkpoint; the extremely small Few Demonstrations dataset constitutes the sole training provenance record and should be fully documented.

FSL. In this framework, the FSL categorization is strictly reserved for parametric approaches—such as gradient-based meta-learning or metric-based networks—where the model's weights undergo a Ad-hoc Permanent, Scheduled Permanent update based on limited labeled data. While the term FSL is colloquially used to describe providing examples in an LLM prompt, this framework classifies that non-parametric, Session-Ephemeral mechanism distinctly as ICL. ICL functions primarily to induce a Knowledge Update, Task Specialization mapping during inference, bypassing parameter updates entirely.