低资源微调用于十亿参数指令调优模型的多任务结构化信息抽取
Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model
AI总结：
本研究提出ETLCH，一个基于LLaMA的十亿参数模型，通过低秩适配在少量样本上微调，用于多任务结构化信息抽取，以低计算成本实现稳定准确输出。
摘要：
在金融合规报告、法律文档分析和多语言知识库构建等领域，部署大型语言模型（LLM）进行结构化数据抽取，对于较小的团队而言往往不切实际，原因是运行大型架构的成本高昂，且准备大规模高质量数据集存在困难。最近的指令调优研究大多聚焦于七十亿参数或更大的模型，对于更小的模型在低资源、多任务条件下能否可靠工作，现有证据有限。本工作提出了ETLCH，一个基于LLaMA的十亿参数模型，通过低秩适配在每任务仅数百到一千个样本上进行微调，用于JSON抽取、知识图谱抽取和命名实体识别。尽管规模较小，ETLCH在大多数评估指标上优于强基线，即使在最低数据规模下也观察到了显著提升。这些发现表明，经过良好调优的小型模型能够以极低的计算成本提供稳定且准确的结构化输出，从而在资源受限环境中实现经济高效且可靠的信息抽取流程。
英文摘要：
Deploying large language models (LLMs) for structured data extraction in domains such as financial compliance reporting, legal document analytics, and multilingual knowledge base construction is often impractical for smaller teams due to the high cost of running large architectures and the difficulty of preparing large, high-quality datasets. Most recent instruction-tuning studies focus on seven-billion-parameter or larger models, leaving limited evidence on whether much smaller models can work reliably under low-resource, multi-task conditions. This work presents ETLCH, a billion-parameter LLaMA-based model fine-tuned with low-rank adaptation on only a few hundred to one thousand samples per task for JSON extraction, knowledge graph extraction, and named entity recognition. Despite its small scale, ETLCH outperforms strong baselines across most evaluation metrics, with substantial gains observed even at the lowest data scale. These findings demonstrate that well-tuned small models can deliver stable and accurate structured outputs at a fraction of the computational cost, enabling cost-effective and reliable information extraction pipelines in resource-constrained environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
