Summary of The Open Source Advantage in Large Language Models (llms), by Jiya Manchanda et al.
The Open Source Advantage in Large Language Models (LLMs)
by Jiya Manchanda, Laura Boettcher, Matheus Westphalen, Jasser Jasser
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have revolutionized natural language processing, achieving breakthroughs in text generation, machine translation, and domain-specific reasoning. The field now faces a dilemma: closed-source models like GPT-4 excel but restrict reproducibility, accessibility, and oversight, while open-source frameworks like LLaMA and Mixtral democratize access, foster collaboration, and support diverse applications through techniques like instruction tuning and LoRA. Hybrid approaches address challenges like bias mitigation and resource accessibility by combining scalability with transparency and inclusivity. This position paper argues that open-source remains the most robust path for advancing LLM research and ethical deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if computers could understand and generate human-like language! Large Language Models (LLMs) have made big progress in this area, helping with tasks like text generation and machine translation. But there’s a problem: some models are closed-source, which means they’re not open to everyone or easy to reproduce. Other models are open-source, making it easier for people to work together and use them for different purposes. This paper thinks that the best way forward is to keep using open-source models because they promote transparency, fairness, and collaboration. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Llama » Lora » Natural language processing » Text generation » Translation
