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[#]: subject: "LLaMA 2 vs GPT-4: What are the Differences?"
[#]: via: "https://www.debugpoint.com/llama-2-vs-gpt-4/"
[#]: author: "Arindam https://www.debugpoint.com/author/admin1/"
[#]: collector: "lkxed"
[#]: translator: "ChatGPT"
[#]: reviewer: "wxy"
[#]: publisher: "wxy"
[#]: url: "https://linux.cn/article-16030-1.html"
Llama 2 vs GPT-4：有何区别？
======
![][0]
> 了解 Llama 2 和 GPT-4 之间的主要区别，它们是自然语言处理的领先巨头。揭示它们的优势、劣势以及它们如何塑造语言技术的未来。
在撰写内容时，有两个关键因素至关重要，“<ruby>困惑度<rt>perplexity</rt></ruby>”和“<ruby>爆发性<rt>burstiness</rt></ruby>”。困惑度衡量文本的复杂程度。而爆发性则比较句子的变化程度。人类倾向于以较大的爆发性写作，例如长句或复杂句与短句并存。人工智能生成的句子往往更加均一。
在自然语言处理领域，Llama 2 和 GPT-4 是两个杰出的参与者，吸引了研究人员和爱好者的关注。这些大型语言模型展示出独特的功能和特点。
虽然 GPT-4 由 OpenAI 已经发布一段时间，但 Meta 与微软合作推出了 Llama 2，这是 LLaMa 扩展语言模型的改进版本。
让我们深入探讨这两个模型之间的关键区别，以了解它们的特点之所在。
### Llama 2：简单易用
Llama 2 是其前身 LLaMa 的升级版本，以其简洁高效的特点震撼了科技界。尽管它支持的语言范围较窄，仅包括 20 种语言，但其性能令人印象深刻，可以与 GPT-4、Claude 或 Bard 等重量级模型相媲美。令人惊讶的是，尽管参数比 GPT-3 模型少，但 Llama 2 可以在单个 GPU 上高效运行，使其成为各种应用的更便捷选择。
Llama 2 真正的特点是它专门训练于公开可获得的数据集，使其对研究人员和开发人员更加可用。更为引人注目的是，尽管仅在 1,000 个精确提示的相对较小数据集上进行训练，它依然实现了有竞争力的结果。
### GPT-4
在 2023 年 3 月，OpenAI 自豪地推出了其最新的创作——GPT-4，这一力作轰动了语言模型领域。GPT-4 在许多任务中表现卓越，包括专业医学和法律考试，展示了其多功能和高水平的能力。
GPT-4 的一个显著特点是相对于之前的版本，它能够扩展最大输入长度。这个增强功能使其能够处理更加广泛和复杂的语言数据，为自然语言理解和生成开辟了新的可能性。
此外，GPT-4 拥有广泛的语言支持，支持 26 种语言。这种多样的语言能力扩大了其在全球范围内的覆盖和适用性，使其成为多语言项目和应用的首选。
### 区别：Llama 2 与 GPT-4
在比较 Llama 2 和 GPT-4 时，我们可以看到两个模型都有各自独特的优缺点。Llama 2 以其简洁高效的特点脱颖而出，尽管其数据集较小且语言支持有限，但其表现卓越。其易用性和有竞争力的结果使其成为某些应用的有力选择。
另一方面，GPT-4 在各种任务上的出色表现和广泛的语言支持使其成为更复杂和多样化项目的强大选择。然而，关于其模型架构和训练数据集的详细信息缺乏，还有一些问题尚待回答。
下表显示了两个模型的一些基准分数（以及其他热门模型）：
| 基准测试 | <ruby>样本数<rt>Shot</rt></ruby> | GPT-3.5 | GPT-4 | PaLM | PaLM-2-L | Llama 2 |
| :- | :- | :- | :- | :- | :- | :- |
| MMLU （5 样本） | 70 | 78.3 | 86.1 | – | – | 86.4 |
| TriviaQA （1 样本） | 69.3 | 33 | 37.5 | – | – | 81.4 |
| Natural Questions （1 样本） | 68.9 | 37.5 | 52.3 | – | – | 85 |
| GSM8K （8 样本） | 85 | 56.5 | 56.8 | – | – | 87 |
| HumanEval （0 样本） | 48.1 | 92 | 56.7 | – | – | 51.2 |
| BIG-Bench Hard （3 样本） | 29.3 | 56.8 | 26.2 | – | – | 29.9 |
### 常见问题解答
1、Llama 2 和 GPT-4 的主要区别是什么？
主要区别在于设计和性能。Llama 2 注重简洁高效，而 GPT-4 具有扩展的输入长度和广泛的语言支持。
2、哪个模型更适合多语言模型？
GPT-4 适用于多语言项目，因为它支持 26 种语言，为全球应用提供了更广泛的范围。
3、Llama 2 可以运行在单个 GPU 上吗？
是的，Llama 2 可以在单个 GPU 上有效运行，使其成为各种应用的实用选择。
4、Llama 2 支持多少种语言？
Llama 2 支持 20 种语言，虽然比 GPT-4 稍少，但仍覆盖了相当广泛的语言范围。
5、GPT-4 是否有可用的基准测试？
不幸的是，没有提及 GPT-4 的具体基准测试，因此对其性能还有一些问题没有答案。
### 结论
Llama 2 和 GPT-4 代表了自然语言处理领域的前沿进展。尽管数据集较小，Llama 2 以其简洁性、易用性和有竞争力的性能令人印象深刻。另一方面，GPT-4 的多功能性、高水平和广泛的语言支持使其成为处理复杂项目的杰出选择。这两个模型对自然语言处理的发展做出了重要贡献，为语言技术在我们生活中发挥更加重要的作用铺平了道路。
基准测试参考：
- MMLU Benchmark (Multi-task Language Understanding): [https://arxiv.org/abs/2009.03300][1]
- Papers With Code: [https://paperswithcode.com/paper/measuring-massive-multitask-language][2]
- GPT-4 Technical Report: [https://arxiv.org/abs/2303.08774][3]
- PaLM: Scaling Language Modeling with Pathways: [https://www.marktechpost.com/2022/04/04/google-ais-latest-540-billion-parameter-model-pathways-language-model-called-palm-unlocks-new-tasks-proportional-to-scale/][4]
- Llama 2: Open Foundation and Fine-Tuned Chat Models: [https://www.youtube.com/watch?v=Xdl_zC1ChRs][5]
*（题图：MJ/60e112f7-3399-49fd-9157-c6b03de5efea）*
--------------------------------------------------------------------------------
via: https://www.debugpoint.com/llama-2-vs-gpt-4/
作者：[Arindam][a]
选题：[lkxed][b]
译者：ChatGPT
校对：[wxy](https://github.com/wxy)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.debugpoint.com/author/admin1/
[b]: https://github.com/lkxed/
[1]: https://arxiv.org/abs/2009.03300
[2]: https://paperswithcode.com/paper/measuring-massive-multitask-language
[3]: https://arxiv.org/abs/2303.08774
[4]: https://www.marktechpost.com/2022/04/04/google-ais-latest-540-billion-parameter-model-pathways-language-model-called-palm-unlocks-new-tasks-proportional-to-scale/
[5]: https://www.youtube.com:443/watch?v=Xdl_zC1ChRs
[6]: https://pixabay.com/users/pezibear-526143/
[0]: https://img.linux.net.cn/data/attachment/album/202307/24/223302fj41272110s7df4c.jpg

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[#]: subject: "LLaMA 2 vs GPT-4: What are the Differences?"
[#]: via: "https://www.debugpoint.com/llama-2-vs-gpt-4/"
[#]: author: "Arindam https://www.debugpoint.com/author/admin1/"
[#]: collector: "lkxed"
[#]: translator: " "
[#]: reviewer: " "
[#]: publisher: " "
[#]: url: " "
LLaMA 2 vs GPT-4: What are the Differences?
======
**Discover the key distinctions between LLaMA 2 and GPT-4, the leading giants of natural language processing. Uncover their strengths, weaknesses and how they shape the future of language technology.**
When it comes to writing content, two factors are crucial, “perplexity” and “burstiness.” Perplexity measures the complexity of the text. Separately, burstiness compares the variations of sentences. Humans tend to write with greater burstiness, for example, with some longer or more complex sentences alongside shorter ones. AI sentences tend to be more uniform.
In the world of natural language processing, two prominent players, LLaMA 2 and GPT-4, have captured the attention of researchers and enthusiasts alike. These large language models (LLMs) showcase their capabilities in diverse ways, each with unique features and functionalities.
While GPT-4 is out for a while by OpenAI, in a surprising collaboration with Microsoft, Meta has launched LLaMA 2, an improved version of its expansive language model, LLaMa.
Let’s delve into the key distinctions between the two models to understand what sets them apart.
### LLaMA 2: Simple and usuable
LLaMA 2, an upgraded version of its predecessor LLaMa, has astounded the tech world with its simplicity and efficiency. Although it supports a narrower range of languages, encompassing 20 languages, its performance is nothing short of impressive and can compete with heavyweight models like GPT-4, Claude, or Bard. Surprisingly, despite having fewer parameters than GPT-3 models, LLaMA 2 can run effectively on a single GPU, making it a more accessible choice for various applications.
What truly sets LLaMA 2 apart is its exclusive training on openly accessible datasets, making it more available to researchers and developers. Even more remarkably, it achieves competitive results despite being trained on a relatively modest dataset of only 1,000 precise prompts.
### GPT-4
In March 2023, OpenAI proudly introduced its latest creation, GPT-4, which took the world of language models by storm. The GPT-4 excels in a multitude of tasks, including professional medical and law exams, showcasing its versatility and proficiency.
One of the defining features of GPT-4 is its ability to expand on the maximum input length compared to its predecessors. This enhancement allows it to process even more extensive and complex language data, opening new avenues for natural language understanding and generation.
Furthermore, GPT-4 boasts extensive language support, accommodating 26 languages. This diverse linguistic capability broadens its global reach and applicability, making it a preferred choice for multilingual projects and applications.
### Differences: LLaMA 2 vs GPT-4
As we compare LLaMA 2 and GPT-4, it becomes evident that both models have their unique strengths and weaknesses. LLaMA 2 stands out with its simplicity and efficiency, performing remarkably well despite its smaller dataset and limited language support. Its accessibility and competitive results make it a compelling option for certain applications.
On the other hand, GPT-4’s impressive performance across various tasks and vast language support make it a formidable choice for more complex and diverse projects. However, the lack of detailed information on its model architecture and training datasets leaves some questions unanswered.
Here are some of the benchmark scores of both models (alongside other popular ones):
| **Benchmark** | **Shots** | **GPT-3.5** | **GPT-4** | **PaLM** | **PaLM-2-L** | **Llama 2** |
| :- | :- | :- | :- | :- | :- | :- |
| MMLU (5-shot) | 70 | 78.3 | 86.1 | – | – | 86.4 |
| TriviaQA (1-shot) | 69.3 | 33 | 37.5 | – | – | 81.4 |
| Natural Questions (1-shot) | 68.9 | 37.5 | 52.3 | – | – | 85 |
| GSM8K (8-shot) | 85 | 56.5 | 56.8 | – | – | 87 |
| HumanEval (0-shot) | 48.1 | 92 | 56.7 | – | – | 51.2 |
| BIG-Bench Hard (3-shot) | 29.3 | 56.8 | 26.2 | – | – | 29.9 |
### FAQs
- The main difference lies in their design and performance. LLaMA 2 focuses on simplicity and efficiency, while GPT-4 boasts expanded input length and extensive language support.
- GPT-4 is more suitable for multilingual projects due to its support for 26 languages, offering a broader scope for global applications.
- Yes, LLaMA 2 can effectively run on a single GPU, making it a practical choice for various applications.
- LLaMA 2 supports 20 languages, which, although narrower than GPT-4, still covers a substantial linguistic range.
- Unfortunately, specific benchmarks for GPT-4 have not been mentioned, leaving some questions about its performance unanswered.
- What is the main difference between LLaMA 2 and GPT-4?
- Which model is more suitable for multilingual projects?
- Can LLaMA 2 run on a single GPU?
- How many languages does LLaMA 2 support?
- Are there any benchmarks available for GPT-4?
### Conclusion
LLaMA 2 and GPT-4 represent cutting-edge advancements in the field of natural language processing. LLaMA 2 impresses with its simplicity, accessibility, and competitive performance despite its smaller dataset. On the other hand, GPT-4’s versatility, proficiency, and expansive language support make it an exceptional choice for complex projects. Both models contribute significantly to the evolution of NLP, paving the way for a future where language technology plays an even more integral role in our lives.
_References for benchmark scores:_
- MMLU Benchmark (Multi-task Language Understanding): [https://arxiv.org/abs/2009.03300][1]
- Papers With Code: [https://paperswithcode.com/paper/measuring-massive-multitask-language][2]
- GPT-4 Technical Report: [https://arxiv.org/abs/2303.08774][3]
- PaLM: Scaling Language Modeling with Pathways: [https://www.marktechpost.com/2022/04/04/google-ais-latest-540-billion-parameter-model-pathways-language-model-called-palm-unlocks-new-tasks-proportional-to-scale/][4]
- Llama 2: Open Foundation and Fine-Tuned Chat Models: [https://www.youtube.com/watch?v=Xdl_zC1ChRs][5]
_Feature image by [Petra][6] from Pixabay._
--------------------------------------------------------------------------------
via: https://www.debugpoint.com/llama-2-vs-gpt-4/
作者：[Arindam][a]
选题：[lkxed][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.debugpoint.com/author/admin1/
[b]: https://github.com/lkxed/
[1]: https://arxiv.org/abs/2009.03300
[2]: https://paperswithcode.com/paper/measuring-massive-multitask-language
[3]: https://arxiv.org/abs/2303.08774
[4]: https://www.marktechpost.com/2022/04/04/google-ais-latest-540-billion-parameter-model-pathways-language-model-called-palm-unlocks-new-tasks-proportional-to-scale/
[5]: https://www.youtube.com:443/watch?v=Xdl_zC1ChRs
[6]: https://pixabay.com/users/pezibear-526143/