3 papers
cs.CL2026
Clusters are All You Need: Pre-Training the Tsetlin Machine with Semantic Clusters from Language Models for Interpretability
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
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)…
cs.CL2026
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
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 transpar…
cs.CL2024
Pruning Literals for Highly Efficient Explainability at Word Level
Rohan Kumar Yadav, Bimal Bhattarai, Abhik Jana +2
Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for…