QM-ToT applies Tree of Thoughts decomposition and evaluator layers to quantized LLMs, reporting accuracy gains from 34% to 50% on MedQAUSMLE for LLaMA2-70b and from 58.77% to 69.49% for LLaMA-3.1-8b, plus an 86.27% improvement in data distillation using only 3.9% of the data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2verdicts
UNVERDICTED 2representative citing papers
A parallel compliance architecture using multi-stage LLM retrieval improves correctness and reasoning quality over a baseline for OT cybersecurity compliance queries in a railway case study.
citing papers explorer
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QM-ToT: A Medical Tree of Thoughts Reasoning Framework for Quantized Model
QM-ToT applies Tree of Thoughts decomposition and evaluator layers to quantized LLMs, reporting accuracy gains from 34% to 50% on MedQAUSMLE for LLaMA2-70b and from 58.77% to 69.49% for LLaMA-3.1-8b, plus an 86.27% improvement in data distillation using only 3.9% of the data.
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Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: A Railway Casestudy
A parallel compliance architecture using multi-stage LLM retrieval improves correctness and reasoning quality over a baseline for OT cybersecurity compliance queries in a railway case study.