LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetlin Machine (TM) offer transparency but lack semantic generalization. We propose a semantic bootstrapping framework that transfers LLM knowledge into symbolic form, combining interpretability with semantic capacity. Given a class label, an LLM generates sub-intents that guide synthetic data creation through a three-stage curriculum (seed, core, enri
Record details
Published: 14 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Transparency
Retrieved: 14 July 2026
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ethics.ai (14 April 2026), “LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines,” evidence record 5898, https://ethics.ai/record/5898 (originally published by arXiv).
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