Small Language Models offer an efficient alternative for structured information extraction.We present SLM-MATRIX,a multi-path collaborative reasoning and verification framework based on SLMs,designed to extract materi...Small Language Models offer an efficient alternative for structured information extraction.We present SLM-MATRIX,a multi-path collaborative reasoning and verification framework based on SLMs,designed to extract material names,numerical values,and physical units from materials science literature.The framework integrates three complementary reasoning paths:a multi-agent collaborative path,a generator–discriminator path,and a dual cross-verification path.SLM-MATRIX achieves an accuracy of 92.85%on the BulkModulus dataset and reaches 77.68%accuracy on the MatSynTriplet dataset,both outperforming conventional methods and single-pathmodels.Moreover,experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability.Ablation studies evaluate the effects of agent number,Mixture-of-Agents(MoA)depth,and discriminator design on overall performance.Overall,SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks.展开更多
文摘Small Language Models offer an efficient alternative for structured information extraction.We present SLM-MATRIX,a multi-path collaborative reasoning and verification framework based on SLMs,designed to extract material names,numerical values,and physical units from materials science literature.The framework integrates three complementary reasoning paths:a multi-agent collaborative path,a generator–discriminator path,and a dual cross-verification path.SLM-MATRIX achieves an accuracy of 92.85%on the BulkModulus dataset and reaches 77.68%accuracy on the MatSynTriplet dataset,both outperforming conventional methods and single-pathmodels.Moreover,experiments on general reasoning benchmarks such as GSM8K and SVAMP validate the framework’s strong generalization capability.Ablation studies evaluate the effects of agent number,Mixture-of-Agents(MoA)depth,and discriminator design on overall performance.Overall,SLM-MATRIX presents an effective approach for high-quality material information extraction in resource-constrained and offers new insights into structured scientific text understanding tasks.
