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原始链接: https://news.ycombinator.com/item?id=43929447

这篇 Hacker News 帖子讨论了“Block Diffusion”论文,该论文探讨了自回归和扩散语言模型的插值。一些评论者表达了将扩散图像生成技术(如 ControlNet)应用于语言模型的潜力。一位用户指出,基于扩散的文本模型并非新鲜事物,但他们有兴趣大规模测试这些模型。 一个反复出现的主题是理解复杂的论文有多难,即使拥有计算机科学学位。评论者们提出了一些建议,建议读者不要气馁,可以考虑学习相关文献或利用 fast.ai 等资源。另一个建议是使用 ChatGPT 来分解概念并逐步加深理解。然而,一些人表达了对 ChatGPT 的准确性和可能出现幻觉的担忧。讨论还涉及到从多篇论文中重新表述信息以帮助理解的重要性。

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  • 原文
    Hacker News new | past | comments | ask | show | jobs | submit login
    Block Diffusion: Interpolating Autoregressive and Diffusion Language Models (m-arriola.com)
    70 points by t55 1 day ago | hide | past | favorite | 14 comments










    Wow.

    I can't wait to see ideas from the diffusion image generation world (like controlnet) work their way into language models.



    I've built diffusion based text models, it's old hat and not necessarily the most performant way to generate text. However it does produce interesting results and I'd love to test some ideas at scale.


    There’s already a few models that are diffusion based.


    Yeah I always end up lost in papers like this too, even with my CS degree, the research keeps leveling up nonstop.


    This was posted here already a few weeks ago.


    Whenever I try to read and understand this paper, I feel extremely dumb. I have my degree in CS, but this is just too complex for me to understand.


    an undergraduate degree in a field is not enough to understand recent research in a specialised subfield of a subfield and you shouldn't beat yourself up over that

    there's nothing wrong with you, you just need the right background and you can go get that. see e.g. the fast.ai course



    Do you mean the fast.ai stable diffusion lectures? The initial series doesn't get too deep at all from what I remember.


    I wouldn’t beat yourself up over it. Very few papers can be understood without reading a significant amount of the neighboring literature and the history of how that work came to be. There are norms and customs and a kind of academic language in every community that you won’t be able to see unless you’ve read a lot from that community. Even if you have the right math level it’s tricky.

    A single paper is part of a conversation, not something that stands alone. Trying to read one random paper is like finding a 1000 page thread on an obscure topic that has been running for 10+ years and reading only the last page. It won’t make any sense without reading back a ways.



    Ask ChatGPT o3 about anything you don't understand, ask it about anything in its responses you don't understand. Keep drilling down until you do understand. Takes patience, but you can learn a lot very fast, this way.


    ChatGPT o3 understands the latest literature and isn't going to hallucinate weird details or make incorrect analogies or math?

    I'd worry about learning the wrong things.



    I disagree. It's all about rephrasing information that is in the paper. Possinly a few other papers too.


    o3 with a pdf or in deep research mode is excellent. Especially if you’re disciplined about staying to what’s research. But really, it’s excellent, better than benchmarks indicate, I’d say.


    Might want to study some stats or other math.






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