The Open Source Advantage in Large Language Models (LLMs)
This position paper addresses the choice of development approach for LLMs, which impacts researchers, developers, and society, but it is incremental as it synthesizes existing arguments without new empirical results.
The paper tackles the dilemma between closed-source and open-source large language models (LLMs), arguing that open-source frameworks like LLaMA and Mixtral offer competitive performance while enhancing reproducibility, accessibility, and ethical deployment.
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.