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ARXIV:2604.12223 · INTERPRETABLE TEXT CLASSIFICATION · SUBMITTED 15 APR · 17:02 UTC · FRESHNESS STALE
ARXIV:2604.12223INTERPRETABLE TEXT CLASSIFICATIONSUBMITTED 15 APR · 17:02 UTCFRESHNESS STALEJiechao Gao · Rohan Kumar Yadav · Yuangang Li · Yuandong Pan · Jie Wang · Ying Liu · +1 at arXiv
A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls.
Opportunity summary
Pain A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls. We propose a semantic bootstrapping framework that transfers LLM knowledge into symbolic form, combining…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Injecting these cues into real data enables a TM to align clause logic with LLM-inferred semantics.
Interpretable Text Classification moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls.
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10.48550/arXiv.2604.12223A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls.
Abstract
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.
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PROBLEM
A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls. We propose a semantic bootstrapping framework that transfers LLM knowledge into symbolic form, combining interpretability with se...
METHOD
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...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Injecting these cues into real data enables a TM to align clause logic with LLM-inferred semantics.
WHY NOW
Interpretable Text Classification moved forward this cycle; last verified April 2026. Public score 3.0/10.
with Tsetlin Machines Jiechao Gao1, Rohan Kumar Yadav2, Yuangang Li3, Yuandong Pan1, Jie Wang1, Ying Liu4, and Michael Lepech1 1Stanford University 2Independent Researcher 3University of California
Implication not extracted yet.
partial
cation—ranging from traditional methods such as bag-of-words (BoW) and TF-IDF, to neural archi- tectures like CNNs and RNNs, and embedding- based models such as fastText and BERT
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partial
et al., 2015; Hochreiter and Schmidhuber, 1997; Dong and de Melo, 2018),fastText(Joulin et al., 2017; Munikar et al., 2019),BERT-base, BERT- large(Peng et al., 2019; Sun et al., 2019; Chen and Miyake, 2021),TM
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deal, proposal, conflict}Enriched input:talks, collapsed, nations, negotiation, agreement, deal, proposal
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Simon Baker, Imran Ali, Ilona Silins, Sampo Pyysalo, Yufan Guo, Johan Högberg, Ulla Stenius, and Anna Korhonen. 2017
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partial
Saeed Rahimi Gorji, Ole-Christoffer Granmo, Adrian Phoulady, and Morten Goodwin Olsen. 2019. A tsetlin machine with multigranular clauses.ArXiv, abs/1909.07310. Ole-Christoffer Granmo. 2018
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partial
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A framework that transfers LLM knowledge into symbolic Tsetlin Machines for interpretable and accurate text classification without runtime LLM calls.
Segment
Interpretable Text Classification
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