开源让大模型更可信、可协作，是推动技术进步的可靠路径。
The Open Source Advantage in Large Language Models (LLMs)
- 主张以开源框架为基础推进大模型研究与应用
- 开源模型通过指令微调等技术实现接近闭源模型的表现
- 适合关注模型透明性与伦理责任的研究者和开发者
大语言模型在文本生成、机器翻译和领域推理等任务中取得显著进展。当前面临关键抉择：闭源模型如GPT-4性能领先但限制可复现性、可及性和外部监督；而开源框架如LLaMA和Mixtral通过指令微调和LoRA等技术，实现竞争力表现，促进协作与多样化应用。混合方案结合闭源系统的扩展性与开源的透明性，缓解偏见与资源不平等问题。本文认为，开源仍是推进大模型研究与伦理部署最稳健的路径。
原文摘要 · Abstract (English)
Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning. The field now faces a critical dilemma in its approach: closed-source models like GPT-4 deliver state-of-the-art performance but restrict reproducibility, accessibility, and external oversight, while open-source frameworks like LLaMA and Mixtral democratize access, foster collaboration, and support diverse applications, achieving competitive results through techniques like instruction tuning and LoRA. Hybrid approaches address challenges like bias mitigation and resource accessibility by combining the scalability of closed-source systems with the transparency and inclusivity of open-source framework. However, in this position paper, we argue that open-source remains the most robust path for advancing LLM research and ethical deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。