通过数据筛选与训练优化，让大模型在资源有限时仍高效可用。
Efficient Strategy for Improving Large Language Model (LLM) Capabilities
- 从基础模型出发，结合数据处理与训练策略提升效率
- 在受限环境下实现性能、响应速度与安全性的平衡
- 适合资源紧张场景下的大模型部署与应用
大语言模型（LLMs）在人工智能与自然语言处理领域取得里程碑进展，但大规模部署仍受制于计算资源需求。本文从基础模型出发，探索并整合数据处理、精细数据筛选、训练策略及架构调整，以提升模型在资源受限环境和限定知识范围内的效率。研究通过定义可靠数据集标准，开展多配置控制实验，并系统评估各变体在能力、通用性、响应时间与安全性方面的表现。最终通过对比测试验证了所提策略的有效性。本工作基于系统与计算机工程专业硕士论文《大语言模型能力提升的高效策略》。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have become a milestone in the field of artificial intelligence and natural language processing. However, their large-scale deployment remains constrained by the need for significant computational resources. This work proposes starting from a base model to explore and combine data processing and careful data selection techniques, training strategies, and architectural adjustments to improve the efficiency of LLMs in resource-constrained environments and within a delimited knowledge base. The methodological approach included defining criteria for building reliable datasets, conducting controlled experiments with different configurations, and systematically evaluating the resulting variants in terms of capability, versatility, response time, and safety. Finally, comparative tests were conducted to measure the performance of the developed variants and to validate the effectiveness of the proposed strategies. This work is based on the master's thesis in Systems and Computer Engineering titled "Efficient Strategy for Improving the Capabilities of Large Language Models (LLMs)".
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