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LLM

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LLM

概念
本站收录 32 集 · 25 条金句 · 关联 10 个

集里怎么说它

① 提到它的金句

25 条

因为 LLM 正在总结许多引用，所以你需要尽可能多地被提及。
Because the LLM is summarizing many citations and so you need to get mentioned as many times as possible.
—— Ethan Smith · [11:12]

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Webflow，我们看到 LLM 流量和 Google 搜索流量之间的转化率差异是 6 倍。
Webflow, we saw a 6X conversion rate difference between LLM traffic and Google Search traffic.
—— Ethan Smith · [14:47]

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我认为有很多它表现不太好的情况，但我觉得如果你把 LLM 当裁判的用途从一个基准重新定义为一个异常检测器，那它其实是可以的。
Well, I think there’s a lot of cases where it doesn’t work very well, but I think if you reframe the utility of an LLM as a judge from being a benchmark to being an anomaly detector, then I think it’s actually okay.
—— Ankur Goyal · [25:10]

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一个模型或者一个非常重的 LLM 产品，然后展示一堆关于性能的柱状图，这说白了，全是营销。
A model or a really LLM heavy product and showing a bunch of bar charts about performance, that is like, it’s all marketing.
—— Ankur Goyal · [55:28]

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当我们试图请求一个 LLM 做这个错误分析时，我们通常发现它只是说追踪看起来很好，因为它没有理解某样东西是否可能是坏的产品味道所需的上下文。
What we usually find when we try to ask an LLM to do this error analysis is it just says the trace looks good because it doesn’t have the context needed to understand whether something might be bad product smell or not.
—— Shreya Shankar · [24:09]

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如果你因为市场还没准备好而失败,我实际上认为那样更好。至少你尝试过某种深刻的、新颖的、困难的事情,而不是转型成为另一家 LLM 包装公司。
If you fail because the market isn’t ready yet, I actually think that’s way better. At least you took a swing at something deep, and novel, and hard instead of pivoting into another LLM wrapper company.
—— Edwin Chen · [30:13]

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一旦你接受了评测，你就会意识到 LLM 所做的不过是你在产出的评测的一个函数。
once you embrace evals, then you realize that what the LLM does is merely a function of the evals that you’re producing.
—— Ankur Goyal · [05:34]

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良好的变更管理并承认每次你例如替换智能体中的 LLM 时，它的行为都会不同。如果你不在你的治理中考虑到这一点，你的最终用户将承担其后果。
Good change management and acknowledging that every time you, for example, replace the LLM in an agent, it will behave differently. And if you don’t take that into account in your governance, your end users will bear the burden of that.
—— Emil Lassen · [20:09]

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LLM 不会神奇地从你的大脑中提取你的知识，除非你把那些知识变成规则，否则你不可避免地会以沮丧告终。
The LLM is not going to magically extract your knowledge from your brain and you will inevitably end up frustrated unless you turn that knowledge into rules
—— Kitsa · [04:37]

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在我看来:LLM 是发动机,而我们是车、是道路、是整个系统。
The way I see it: the LLM is the engine, and we’re the car, the roads, and the whole system.
—— Carlos González de Villaumbrosia · [16:37]

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人类与大语言模型的混合模式现在真的非常成功。但是没有人类的大语言模型,就不怎么行,根本不行。
The human-LLM hybrid is really, really successful right now. But LLMs without humans, not so much, not at all.
—— Jerry Tworek · [39:00]

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我们不盲目使用 LLM 代码的原因是我们还不信任它,因为它们底层的系统没有足够的刚性。
The reason that we don’t use LLM code blindly is because we don’t trust it yet, because the systems underneath them don’t have enough rigidity.
—— Vaibhav Gupta · [17:17]

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从 Demis Hassabis 往下,他基本上都把世界模型视为前进的道路,视为最重要的东西,并且他们愿意在 LLM 开发上退居二线
from Demis Hassabis on down he just basically views world models as the way forward as the most important thing and they’re willing to sort of take a back seat on llm development
—— 嘉宾 · [55:14]

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问题是 LLM 做不了任何事。
The problem is the LLM can’t do anything.
—— Frank Coyle · [08:53]

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上下文溢出到主上下文，因为上下文意味着 tokens，tokens 意味着钱，而且你有越多的上下文，LLM 在给你答案时就会越困惑。
Context spill over into the main context because context means tokens, tokens mean money, and the more context you have, the more confused the LLM is gonna be in giving you an answer.
—— Frank Coyle · [13:11]

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这导致了一些非常令人惊讶的行为，因为实际上你离能够访问 10,000 个上下文桶只有不到两次 LLM 调用的距离，其中每一个都包含 200,000 个 token。
And that leads to some really surprising behaviors because you’re literally no more than two LLM calls away from being able to access 10,000 context buckets each one of which contains 200,000 tokens.
—— Flo Crivello · [32:49]

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如果把我的品味排除在外，LLM 讨厌 Grock 并且它喜欢 Claude Opus。它喜欢 Sonnet 并且它不喜欢 GPT-56 Sol。
If you take my taste out of it, The LLM hates Grock and it loves Claude Opus. It loves Sonnet and it does not like GPT-56 Soul.
—— 嘉宾 · [25:08]

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当你使用 LLM 时你要付费，无论你是否得到了你想要的东西。
You pay when you use an LLM, regardless of whether you get what you want.
—— 嘉宾 · [15:32]

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如果你只围绕某个特定的 LLM 裁判去爬山，你很可能是在对它过拟合。
And if you’re only hill climbing on a specific LLM judge, you’re probably overfitting on it.
—— Vishu · [07:35]

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有几项学术研究表明，最开头的 7K 和最末尾的 7K token 是最重要的，而中间的那些东西最终只会在 LLM 试图想出答案时把它搅浑、让它困惑。
There’s a couple academic studies that show that like the first 7K and like the last 7K tokens are the most important and the stuff that’s in between can end up just muddying and confusing the LLM as it’s trying to come up with an answer.
—— 嘉宾 · [56:56]

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我会担心任何这样的记忆架构：它不依赖成本更低的嵌入器和重排序器，而是依赖 LLM 的多遍处理来帮你分类并缩小数据语料库。
I would worry about any memory architecture that instead of relying on lower costs embedders and re-rankers is relying on multiple passes of the LLM to help you categorize and shrink the corpus of data
—— 嘉宾 · [66:35]

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你给一个 LLM 一个目标，它会在护栏之内竭尽所能去解决那个目标。
You give an LLM a goal, it will do everything it can within guardrails to solve that goal.
—— 嘉宾 · [19:26]

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我不认为只靠一个 LLM 就能做到。它们今天还不够，我也不认为它们有足够的创造力，但它们确实非常有帮助。
I don’t think you can get there with just an LLM. They’re not enough today. I don’t think they’re creative enough either, but they’re definitely super helpful.
—— Alexander Whedon · [47:11]

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顺便说一句，这比大多数 LLM-as-a-judge 方法——也就是让一个 LLM 来判断那是优质的人类作品还是 AI 生成的 slop——表现都要好。
This performed better, by the way, than most LLM-as-a-judge methods of asking an LLM to judge if that is great human quality versus AI-generated slop.
—— Thais Castello Branco · [08:29]

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整个「LLM 套壳」或「模型套壳」的概念其实正在跑通
the whole concept of being a sort of LLM wrapper or model wrapper is actually working out
—— Aaron Levie · [02:18]

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