Computation and Language - DeepSeek LLM Scaling Open-Source Language Models with Longtermism

Hey PaperLedge crew, Ernis here! Get ready to dive into some fascinating research about the brains behind the bots – Large Language Models, or LLMs! We’re talking about the tech that powers things like ChatGPT, but today we're digging into a new player in the open-source world: DeepSeek LLM.

Now, you've probably heard about how these AI models just keep getting bigger and better. But there's a catch! There's this idea called a "scaling law" that tries to predict how well an LLM will perform based on its size and the amount of data it's trained on. Think of it like this: imagine you’re baking a cake. The scaling law is like the recipe, telling you how much flour and sugar you need for the best results. But the "recipes" we have for LLMs seem to disagree! Some say bigger is always better, others are more skeptical.

This paper from the DeepSeek team dives headfirst into these scaling laws to figure out the optimal recipe for building powerful LLMs. They specifically focused on two popular sizes for open-source LLMs: 7 billion parameters and 67 billion parameters. Parameters are like the little knobs and dials inside the AI that it uses to learn and understand language – the more knobs, the more complex it can be.

So, what did they do? Well, they built DeepSeek LLM! Think of it as their own open-source challenger to the big names like LLaMA. To train it, they created a massive dataset – currently at a whopping 2 trillion tokens and growing! A token is basically a piece of a word, and 2 trillion is an enormous amount of text and code for the AI to learn from. Imagine reading every book ever written, multiple times over!

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But just having a big brain isn't enough, right? You need to teach it how to use that brain. So, the DeepSeek team did two things:

• Supervised Fine-Tuning (SFT): This is like giving the AI a personalized tutor. They showed it examples of good conversations and asked it to mimic them. Think of it as teaching a dog to fetch by showing it exactly what you want it to do.
• Direct Preference Optimization (DPO): This is where they fine-tuned the AI based on what humans actually preferred. They presented the AI with two possible responses to a question and asked people which one they liked better. It's like teaching a dog to sit by giving it treats when it sits correctly, and ignoring it when it doesn't.

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The results? DeepSeek LLM 67B outperformed LLaMA-2 70B, another really strong open-source model, on a bunch of tests! It was particularly good at coding, math, and reasoning. They even did some open-ended tests where they just asked the AI to chat and found that DeepSeek LLM 67B was even better than GPT-3.5 in many ways! That's a pretty big deal!

So, why does this matter? Here's the breakdown:

• For developers: This gives you a powerful, open-source tool to build amazing AI applications without being locked into proprietary systems. Think of it as having access to a high-performance engine that you can customize and tweak to your exact needs.
• For researchers: This helps us better understand how to build and train LLMs, pushing the boundaries of what's possible with AI. It gives them more data points to refine those "scaling law recipes."
• For everyone else: This shows us that AI is becoming more accessible and that open-source development can lead to powerful, innovative technologies. It means more people have a say in the future of AI.

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This research is a big step forward in making powerful AI technology more accessible. It shows that with careful attention to scaling laws and a commitment to open-source development, we can build amazing tools that benefit everyone.

Now, a few things that popped into my head while I was reading this:

• If DeepSeek outperformed GPT-3.5, how close is it to GPT-4, and what are the implications for open-source AI competing with closed-source giants?
• How can we ensure that these powerful open-source models are used responsibly and ethically, especially given their capabilities in areas like coding?
• With the dataset growing so rapidly, how do they ensure its quality and avoid biases that could creep into the model's behavior?

Alright, that's the DeepSeek LLM paper in a nutshell! Let me know what you guys think! What other questions does it raise for you?

Credit to Paper authors: DeepSeek-AI, :, Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, Huazuo Gao, Kaige Gao, Wenjun Gao, Ruiqi Ge, Kang Guan, Daya Guo, Jianzhong Guo, Guangbo Hao, Zhewen Hao, Ying He, Wenjie Hu, Panpan Huang, Erhang Li, Guowei Li, Jiashi Li, Yao Li, Y. K. Li, Wenfeng Liang, Fangyun Lin, A. X. Liu, Bo Liu, Wen Liu, Xiaodong Liu, Xin Liu, Yiyuan Liu, Haoyu Lu, Shanghao Lu, Fuli Luo, Shirong Ma, Xiaotao Nie, Tian Pei, Yishi Piao, Junjie Qiu, Hui Qu, Tongzheng Ren, Zehui Ren, Chong Ruan, Zhangli Sha, Zhihong Shao, Junxiao Song, Xuecheng Su, Jingxiang Sun, Yaofeng Sun, Minghui Tang, Bingxuan Wang, Peiyi Wang, Shiyu Wang, Yaohui Wang, Yongji Wang, Tong Wu, Y. Wu, Xin Xie, Zhenda Xie, Ziwei Xie, Yiliang Xiong, Hanwei Xu, R. X. Xu, Yanhong Xu, Dejian Yang, Yuxiang You, Shuiping Yu, Xingkai Yu, B. Zhang, Haowei Zhang, Lecong Zhang, Liyue Zhang, Mingchuan Zhang, Minghua Zhang, Wentao Zhang, Yichao Zhang, Chenggang Zhao, Yao Zhao, Shangyan Zhou, Shunfeng Zhou, Qihao Zhu, Yuheng Zou