The social contract of writing

写作的社会契约

LLMs are making inroads into just about every industry on the planet, they’re everywhere now. AI for X, AI for Y, if there’s a thing that somebody is willing to pay for, there’s another person looking for a way to use LLMs to do it. But no human activity is becoming as dominated by LLMs as writing. 大语言模型（LLMs）正在渗透到全球的每一个行业，它们现在无处不在。“AI+X”、“AI+Y”，只要有人愿意买单，就有人在寻找利用大语言模型来实现的方法。但在所有人类活动中，没有哪一项像写作这样正被大语言模型所主导。

It’s not that I can’t see the attraction of it as an author, especially where you feel a pressure to produce a lot of content. They’re very good at that, volume. I’ve experimented with LLM assisted writing in the past (nowadays I don’t even use them for spell-checking). People use LLMs to assist them in writing on blogs, social media, newspapers, books, and they use them for spell checking, grammar, fact checking, and unfortunately, in way too many cases, to just write the whole thing outright. 作为一名作者，我并非不能理解它的吸引力，尤其是在你感到需要产出大量内容时。它们在“量”这一方面确实非常出色。我过去曾尝试过大语言模型辅助写作（如今我连拼写检查都不用它们了）。人们使用大语言模型来辅助撰写博客、社交媒体、报纸和书籍，用它们进行拼写检查、语法纠错、事实核查，而不幸的是，在太多情况下，人们直接用它们代写全文。

Once you learn to recognize the idioms and idiosyncrasies of LLM writing, you can’t stop seeing it. It’s everywhere. And it’s exhausting. Even worse, it’s boring. All writing is homogenizing, slowly turning into the same slop. You see the same patterns everywhere, “it’s not x, it’s why”, em-dashes, or why not: “you’re not imagining it, the problem is real”. That last one actually drives me over the wall, I don’t know why, I just can’t stand it. 一旦你学会识别大语言模型写作的惯用语和怪癖，你就会发现它们无处不在。这让人精疲力竭。更糟糕的是，它很无聊。所有的写作都在趋同，慢慢变成同样的“垃圾”。你到处都能看到相同的模式，比如“不是 X，而是 Y”，破折号的使用，或者“你不是在胡思乱想，问题确实存在”。最后这一句简直让我抓狂，我不知道为什么，我就是无法忍受。

Increasingly everyone is having a strong negative reaction to this mass produced slop. It’s infuriating to invest time into reading something only to realize the author didn’t invest the corresponding amount of time into writing it. What’s interesting is that this is true even where the content itself might actually be fine. Correct, properly researched, it doesn’t matter. 越来越多的人对这种批量生产的“垃圾”产生了强烈的抵触情绪。当你投入时间阅读某篇文章，却发现作者并没有投入相应的时间去撰写它时，这令人愤怒。有趣的是，即使内容本身可能没问题——准确且经过充分研究——情况依然如此，这并不重要。

Oxide RFD 576 This was the first thing I read that I felt like really articulated the problem. Oxide Computers have this wonderful convention of writing long form documents for enabling discussions and establishing conventions, Request for Discussion(s), and many of them are public. RFD 576 deals with the use of LLMs. The part specifically that’s relevant here is section 2.4, LLMs as writers. Oxide RFD 576 是我读过的第一篇真正阐述了这一问题的文章。Oxide Computers 有一个很棒的传统，即撰写长篇文档来促进讨论和建立规范，即“讨论请求”（RFD），其中许多是公开的。RFD 576 探讨了大语言模型的使用。其中与本文特别相关的是 2.4 节：“作为写作者的大语言模型”。

Finally, LLM-generated prose undermines a social contract of sorts: absent LLMs, it is presumed that of the reader and the writer, it is the writer that has undertaken the greater intellectual exertion. (That is, it is more work to write than to read!) For the reader, this is important: should they struggle with an idea, they can reasonably assume that the writer themselves understands it — and it is the least a reader can do to labor to make sense of it. 最后，大语言模型生成的散文破坏了一种社会契约：在没有大语言模型的情况下，人们默认在读者和作者之间，作者承担了更大的智力劳动。（也就是说，写比读更费力！）对于读者来说，这一点很重要：如果他们对某个观点感到困惑，他们可以合理地假设作者本人是理解它的——而读者努力去理解它，是他们所能做的最起码的事情。

So in fact it doesn’t matter whether the content is good, or even that the writing is fine, it’s the action of using an LLM to write instead of writing yourself. The very fact that the author reduced the effort they made to produce the content is a violation of the social contract. You can’t avoid it. 所以事实上，内容好不好，甚至文笔好不好都不重要，重要的是使用大语言模型代替自己写作这一行为。作者为了产出内容而减少自身努力这一事实，本身就是对社会契约的违背。你无法回避这一点。

Even if you’re avoiding using LLMs to write, you’re likely still being affected by the torrent of generated text. Apart from using LLM language to make fun of LLMs, like the ubiquitous “you’re absolutely right”, these tools are changing how we speak in subtle ways. A study at the Max-Planck Institute for Human Development showed ChatGPT’s penchant for specific words increased their prevalence even in spoken human language, increasing the frequency of words like delve, realm, meticulous, adept, boast, swift, and comprehend. 即使你避免使用大语言模型写作，你也可能仍会受到生成文本洪流的影响。除了用大语言模型的语言来调侃大语言模型（比如随处可见的“你说得完全正确”），这些工具正在以微妙的方式改变我们的说话方式。马克斯·普朗克人类发展研究所的一项研究表明，ChatGPT 对特定词汇的偏好增加了它们在人类口语中的流行度，增加了诸如 delve（钻研）、realm（领域）、meticulous（一丝不苟）、adept（熟练）、boast（夸耀）、swift（迅速）和 comprehend（理解）等词汇的使用频率。

Even if you’re not directly using it, the products of generative AI are everywhere. Low-background steel is the name for steel produced before the detonation of the first atomic bombs, and is increasingly sought after. The many nuclear tests during the 1940s and 50s filled the atmosphere with enough radioactive materials to taint the entire surface of the planet and steel produced after that point is not “clean” enough for certain applications, like particle detectors. 即使你没有直接使用它，生成式 AI 的产物也无处不在。“低本底钢”是指在第一次原子弹爆炸前生产的钢材，这种钢材正变得越来越抢手。20 世纪 40 年代和 50 年代的多次核试验向大气中排放了足够的放射性物质，污染了整个地球表面，此后生产的钢材对于某些应用（如粒子探测器）来说不够“纯净”。

Okay, turns out, that’s not quite true anymore. Global anthropogenic background radiation has apparently dropped low enough that recently produced steel can be used for most of these things now. But let’s not let that get in the way of a good metaphor. Anything written after November 30, 2022 is to some degree affected by the proliferation of LLMs. You can’t get around that, other than by exclusively reading old content. 好吧，事实证明，这已经不再完全正确了。全球人为背景辐射显然已经降到了足够低的水平，最近生产的钢材现在可以用于大多数此类用途。但我们不要让事实阻碍了一个好的比喻。2022 年 11 月 30 日之后撰写的任何内容，在某种程度上都受到了大语言模型普及的影响。除了只读旧内容，你无法绕过这一点。

Writing in the post-LLM world: Subtle taint aside, there will only be an increasing demand for original thought and expression, both from individual humans, and from the model companies to use as training material. The ability to write original content, without LLMs, will just become more valuable as the generated content takes over more and more of the internet. I guess the hard part will be finding it in the constant onslaught of LinkedIn thought leadership posts and AI generated cat pictures. 后大语言模型时代的写作：撇开微妙的污染不谈，对原创思想和表达的需求只会越来越大，无论是来自个人，还是来自需要将其作为训练材料的模型公司。随着生成式内容占据互联网的比例越来越高，不依赖大语言模型进行原创写作的能力将变得更加宝贵。我想，难点在于如何在 LinkedIn 上源源不断的“思想领袖”文章和 AI 生成的猫咪图片中找到这些原创内容。

One of the most interesting consequences of this is how it’s affecting what we consider good writing. For as long as humanity has had grammar, and writing, we’ve cared about it being done well. We’ve put a premium on good grammar, vast vocabulary, good use of expressions and metaphors, and general text composition. LLMs do all of that just fine. 这带来的最有趣的结果之一是，它正在影响我们对“好文章”的定义。自从人类有了语法和写作以来，我们就一直追求写得好。我们重视良好的语法、丰富的词汇、对表达和隐喻的巧妙运用，以及整体的文本结构。大语言模型在这些方面做得很好。

Sure, they just won’t stop repeating the same patterns, the expressions are tired, the metaphors are a bit out there, and they’ve given the em-dash a bad name. But the reality is that students today in school have the option of either working hard and get an average grade, or do no work at all, have ChatGPT write the paper, and get a top score. 当然，它们总是重复相同的模式，表达陈旧，隐喻有些离谱，而且它们让破折号的使用变得声名狼藉。但现实是，今天的学生面临着选择：要么努力学习拿到平均分，要么什么都不做，让 ChatGPT 代写论文，然后拿到高分。

Take the writing of Claude today and show it to someone 10 years ago, I doubt they’d have that much to complain about. It’s repetitive over time, when you’ve read enough of it, but it does match a lot of the traditional criteria of “proper” writing. Not Nobel prize winning, but fine. 拿今天 Claude 写的东西给 10 年前的人看，我怀疑他们不会有太多抱怨。当你读得足够多时，会发现它在时间维度上是重复的，但它确实符合许多关于“规范”写作的传统标准。虽然拿不到诺贝尔奖，但还算不错。

But today what I crave is original expression. I don’t care if the grammar is wrong, as long as it’s different. I don’t care if the vocabulary is limited, just don’t use the word “delve”, please. Instead of looking down on an author for typos, I’ll cherish every single one. I don’t want anymore of the bland. 但今天，我渴望的是原创表达。我不在乎语法是否错误，只要它与众不同就行。我不在乎词汇量是否有限，只要别用“delve”这个词，拜托。与其因为错别字而看不起作者，我反而会珍惜每一个错别字。我再也不想看到那些平庸乏味的东西了。