少点错误 2024年11月01日
JargonBot Beta Test
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LessWrong推出'Automated Jargon Glossaries'新功能,当作者保存草稿时,数据库会查询语言模型来识别读者可能不知道的术语并解释。该功能考虑了作者审批、读者体验、长期愿景等多方面,旨在提升阅读和理解技术内容的便利性,但也存在一些担忧。

🎯作者方面,系统为作者提供可能有用且准确的术语短列表以供审查,同时作者可访问默认隐藏的不太有用的术语,并能编辑术语表提示以适应其偏好风格。

👀读者方面,默认只看到作者批准的高信号术语表术语,首次看到的术语会稍显灰色可悬停查看,且可选择突出显示所有术语或查看所有术语。

🌈长期愿景包括根据读者水平自动调整突出显示的术语,为不同领域的读者提供所需信息,还可能提供'像我12岁时那样解释'的按钮,以及对LaTeX的悬停解释等。

💡提到当前AI虽能解释简单事物,但在思考大型概念如何组合方面不太擅长,希望未来工具能在这方面有所改善,同时也表达了对AI发展过快的担忧。

Published on November 1, 2024 1:05 AM GMT

We've just launched a new experimental feature: "Automated Jargon Glossaries." If it goes well, it may pave the way for things like LaTeX hoverovers and other nice things.

Whenever an author with 100+ karma saves a draft of a post, our database queries a language model to:

By default, explanations are not shown to readers. Authors get to manually approve term/explanations that they like, or edit them. Authors will see a UI looking like this, allowing them to enable terms so readers see them. They can also edit them (by clicking on the term).

Meanwhile, here's a demo of what readers might see, from the Infra-Bayesian physicalism post.[1]

TLDR: We present a new formal decision theory that realizes naturalized induction. Our agents reason in terms of infra-Bayesian hypotheses, the domain of which is the cartesian product of computations and physical states, where the ontology of "physical states" may vary from one hypothesis to another. The key mathematical building block is the "bridge transform", which, given such a hypothesis, extends its domain to "physically manifest facts about computations". Roughly speaking, the bridge transforms determines which computations are executed by the physical universe. In particular, this allows "locating the agent in the universe" by determining on which inputs its own source is executed.

0. Background

The "standard model" of ideal agency is Bayesian reinforcement learning, and more specifically, AIXI. We challenged this model before due to its problems with non-realizability, suggesting infra-Bayesianism as an alternative. Both formalisms assume the "cartesian cybernetic framework", in which (i) the universe is crisply divided into "agent" and "environment" and (ii) the two parts interact solely via the agent producing actions which influence the environment and the environment producing observations for the agent. This is already somewhat objectionable on the grounds that this division is not a clearly well-defined property of the physical universe. Moreover, once we examine the structure of the hypothesis such an agent is expected to learn (at least naively), we run into some concrete problems.

Managing Slop: Author Approval & Opt In

I take pretty seriously the worry that LessWrong will become filled with AI slop, and that people will learn to tune out UI features built around it. Longterm, as AI gets good enough to not be slop, I'm even more worried, since then it might get things subtly wrong and it'd be really embarrassing if the AI Alignment discourse center plugged AIs into its group cognition and then didn't notice subtle errors.

These problems both seem tractable to me to deal with, but do require a bunch of effort and care. 

For now: we've tried to tune the generation to minimize annoying "false positives" (terms which are too basic or sufficiently obvious from context) for authors or readers, while setting things up so it's possible to notice "false negatives" (perfectly good terms that the system rejected).

The current system is that authors have:

Meanwhile, readers experience:

I'm not sure whether it's correct to let most users see the "hidden potential-slop", but I'm somewhat worried that authors won't actually approve enough terms on the margin for more intermediate-level readers. It seems okay to me to let readers opt-in, but, I'm interested in how everyone feels about that.

The longterm vision

The Lightcone team has different visions about whether/how to leverage LLMs on LessWrong. Speaking only for myself, here are some things I'm (cautiously) excited about:

Automated "adjust to reader level." 

Readers who are completely new to LessWrong might see more basic terms highlighted. We've tried to tune the system so it doesn't, by default, explain words like 'bayesian' since it'd be annoying to longterm readers, but newcomers might actively want those.

People who are new to the field of Machine Learning might want to know what a ReLU is. Experienced ML people probably don't care.

I think the mature version of this a) makes a reasonable guess about what terms you'll want highlighted by default, b) lets you configure it yourself.

If you're reading a highly technical post in a domain you don't know, eventually we might want to have an optional "explain like I'm 12" button at the top, that takes all the author-approved-terms and assembles them into an introduction, that gives you background context on what this field and author are trying to accomplish, before diving into the cutting-edge details.

The 0-1 second level

Some other LW team members are less into JargonBot, because they were already having a pretty fine time asking LLMs "hey what's this word mean?" while reading dense posts. I'm not satisfied with that, because I think there's a pretty big difference between "actions that take 5-10 seconds" and "actions that take 0-1 second". 

Actions in the 0-1 second zone can be a first-class part of my exobrain – if I see something I don't know, I personally want to briefly hover over it, get a sense of it, and then quickly move back to whatever other sentence I was reading.

The 0-1 second level is also, correspondingly, more scary, from the standpoint of 'integrating AI into your thought process.' I don't currently feel worried about it for JargonBot in particular (in particular since it warns when a thing is AI generated).

I do feel much more worried about it for writing rather than reading, since things like "autocomplete" more actively insert themselves into your thinking loop. I'm interested in takes on this (both for JargonBot and potential future tools)

LaTeX

This started with a vision for "hoverovers for LaTeX", such that you could easily remember what each term in an equation means, and what each overall chunk of the equation represents. I'm pretty excited for a LessWrong that actively helps you think through complex, technical concepts.

Curating technical posts

Currently, posts are more likely to get curated if they are easier to read – simply because easier to read things get read more. Periodically a technical post seems particularly important, and I sit down and put in more effort to slog through it and make sure I understand it so I can write a real curation notice, but it's like a 5-10 hour job for me. (Some other LW staff find it easier, but I think even the more technically literate staff curate fewer technical things on the margin)

I'm excited for a world where the LW ecosystem has an easier time rewarding dense, technical work, which turns abstract concepts into engineering powertools. I'm hoping JargonBot both makes it easier for me to read and curate things, as well as easier for readers to read them (we think of ourselves as having some kinda "budget" for curating hard-to-read things, since most of the 30,000 people on the curation mailing list probably wouldn't actually get much out of them).

Higher level distillation

Right now, AIs are good at explaining simple things, and not very good at thinking about how large concepts fit together. It currently feels like o1 is juuuuust on the edge of being able to do a good job with this. 

It's plausible than in ~6 months the tools will naturally be good enough (and we'll have figured out how to leverage them into good UI) that in addition to individual terms, AI tools can assist with understanding the higher-level bits of posts and longterm research agendas. ("WTF is Infrabayesianism for, again?")

Remember AI capabilities are probably bad, actually

Despite all that, I do remind myself that although a lot of this is, like, objectively cool, also, man I do really wish the frontier labs would coordinate to slow down and lobby the government to help them do it. I'm really worried about how fast this is changing, and poorly I expect humanity to handle the situation.

As I've thought more about how to leverage AI tools, I've also somewhat upweighted how much I try to prioritize thinking about coordinated AI pause/slowdown, so that my brain doesn't naturally drift towards fewer marginal thoughts about that.

Feedback

With all that in mind, I would like to hear from LW users:

Let us know what you think!

  1. ^

    (Note: I have not yet checked with @Vanessa Kosoy if the initial definition here is accurate. On the real post, it won't appear for readers until the author deliberately enabled it. I've enabled it on this particular post, for demonstration purposes. But, warning! It might be wrong!)



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