Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability

• URL: http://arxiv.org/abs/2606.19815v1
• Date: Thu, 18 Jun 2026 05:43:05 GMT
• Title: Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
• Abstract summary: Pre-trained language models such as BERT achieve strong text classification performance but lack transparency.<n>We propose a semantic pre-training framework that transfers knowledge from a pre-trained language model into a TM without using embeddings.
• Score: 9.034780450032768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained language models such as BERT achieve strong text classification performance but lack transparency, limiting their use in high-stakes settings. The Tsetlin Machine (TM) offers fully interpretable, clause-based reasoning but captures little semantic information, and prior attempts to bridge the two rely on static word embeddings that miss contextual meaning. We propose a semantic pre-training framework that transfers knowledge from a pre-trained language model into a TM without using embeddings. Text samples are grouped into semantically coherent clusters with K-means or Top2Vec, and the resulting cluster-sample pairs pre-train a non-negated TM with enhanced Type I feedback. The TM thereby learns interpretable semantic keywords that are fine-tuned on downstream tasks. Across five datasets, our method substantially outperforms vanilla and embedding-based TMs and reaches performance competitive with BERT while remaining interpretable.

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