The Open Source Advantage in Large Language Models (LLMs)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918159819210752 |
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| author | Manchanda, Jiya Boettcher, Laura Westphalen, Matheus Jasser, Jasser |
| author_facet | Manchanda, Jiya Boettcher, Laura Westphalen, Matheus Jasser, Jasser |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Open Source Advantage in Large Language Models (LLMs) Manchanda, Jiya Boettcher, Laura Westphalen, Matheus Jasser, Jasser Computation and Language Machine Learning I.2.7 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. |
| title | The Open Source Advantage in Large Language Models (LLMs) |
| topic | Computation and Language Machine Learning I.2.7 |
| url | https://arxiv.org/abs/2412.12004 |