LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915936288636928 |
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| author | Gao, Jiechao Yadav, Rohan Kumar Li, Yuangang Pan, Yuandong Wang, Jie Liu, Ying Lepech, Michael |
| author_facet | Gao, Jiechao Yadav, Rohan Kumar Li, Yuangang Pan, Yuandong Wang, Jie Liu, Ying Lepech, Michael |
| contents | 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, enriched), expanding semantic diversity. A Non-Negated TM (NTM) learns from these examples to extract high-confidence literals as interpretable semantic cues. Injecting these cues into real data enables a TM to align clause logic with LLM-inferred semantics. Our method requires no embeddings or runtime LLM calls, yet equips symbolic models with pretrained semantic priors. Across multiple text classification tasks, it improves interpretability and accuracy over vanilla TM, achieving performance comparable to BERT while remaining fully symbolic and efficient. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12223 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines Gao, Jiechao Yadav, Rohan Kumar Li, Yuangang Pan, Yuandong Wang, Jie Liu, Ying Lepech, Michael Computation and Language Artificial Intelligence Machine Learning 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, enriched), expanding semantic diversity. A Non-Negated TM (NTM) learns from these examples to extract high-confidence literals as interpretable semantic cues. Injecting these cues into real data enables a TM to align clause logic with LLM-inferred semantics. Our method requires no embeddings or runtime LLM calls, yet equips symbolic models with pretrained semantic priors. Across multiple text classification tasks, it improves interpretability and accuracy over vanilla TM, achieving performance comparable to BERT while remaining fully symbolic and efficient. |
| title | LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.12223 |