大型語言模型（LLMs）中的開源優勢

The Open Source Advantage in Large Language Models (LLMs)

December 16, 2024

作者: Jiya Manchanda, Laura Boettcher, Matheus Westphalen, Jasser Jasser

cs.AI

摘要

大型語言模型（LLMs）標誌著自然語言處理（NLP）的重要轉變，已在文本生成、翻譯和特定領域推理方面取得了進展。像 GPT-4 這樣的封閉源模型，依賴專有數據集和龐大的計算資源，目前在性能方面處於領先地位。然而，它們因「黑盒」特性和限制可訪問性而受到批評，這種限制阻礙了可重現性和公平的人工智能發展。相比之下，像 LLaMA 和 BLOOM 這樣的開源倡議通過社區驅動的開發和計算效率來優先考慮民主化。這些模型在語言多樣性和特定領域應用方面顯著縮小了性能差距，同時為全球研究人員和開發人員提供了可訪問的工具。值得注意的是，這兩種範式都依賴於基礎架構創新，例如 Vaswani 等人（2017）提出的 Transformer 框架。封閉源模型在規模化方面表現出色，而開源模型則適應了少數語言和領域的實際應用。像低秩適應（LoRA）和指令調整數據集這樣的技術使開源模型在資源有限的情況下實現了競爭性結果。確實，封閉源和開源方法之間的張力凸顯了人工智能中透明度與專有控制的更廣泛辯論。道德考量進一步突顯了這種分歧。封閉源系統限制了外部審查，而開源模型促進了可重現性和協作，但缺乏標準化的審計文檔框架來減輕偏見。整合兩種範式優勢的混合方法可能塑造了LLM創新的未來，確保可訪問性、競爭性技術性能和道德部署。

English

Large language models (LLMs) mark a key shift in natural language processing (NLP), having advanced text generation, translation, and domain-specific reasoning. Closed-source models like GPT-4, powered by proprietary datasets and extensive computational resources, lead with state-of-the-art performance today. However, they face criticism for their "black box" nature and for limiting accessibility in a manner that hinders reproducibility and equitable AI development. By contrast, open-source initiatives like LLaMA and BLOOM prioritize democratization through community-driven development and computational efficiency. These models have significantly reduced performance gaps, particularly in linguistic diversity and domain-specific applications, while providing accessible tools for global researchers and developers. Notably, both paradigms rely on foundational architectural innovations, such as the Transformer framework by Vaswani et al. (2017). Closed-source models excel by scaling effectively, while open-source models adapt to real-world applications in underrepresented languages and domains. Techniques like Low-Rank Adaptation (LoRA) and instruction-tuning datasets enable open-source models to achieve competitive results despite limited resources. To be sure, the tension between closed-source and open-source approaches underscores a broader debate on transparency versus proprietary control in AI. Ethical considerations further highlight this divide. Closed-source systems restrict external scrutiny, while open-source models promote reproducibility and collaboration but lack standardized auditing documentation frameworks to mitigate biases. Hybrid approaches that leverage the strengths of both paradigms are likely to shape the future of LLM innovation, ensuring accessibility, competitive technical performance, and ethical deployment.