The generative AIs, the chatbots, the LLMs, all kinds of applications are emerging.

生成式人工智能、聊天机器人、法学硕士以及各种应用程序正在涌现。

Both sides of the argument are marshalling LLMs to make their case.

争论的双方都带领着大型语言模型来证明他们的观点。

But it is safer to call what LLMs do " pseudo-cognition" .

但将大型语言模型所做的事称为“伪认知”是更为安全的说法。

They're all based on large language models, or LLMs, which predict the next word in a sentence.

它们都基于大型语言模型(LLM)，大型语言模型可以预测句子中的下一个单词。

Linguists have, however, found clever ways to test LLMs' underlying knowledge, in effect tricking them with probing tests.

然而，语言学家已经找到了测试大型语言模型的潜在知识的巧妙办法，实际上就是用试探性的测试来欺骗它们。

And some are now seizing on the capacities of LLMs to attack Chomskyan theories anew.

其中一些人现在正利用大型语言模型的能力来对乔姆斯基的理论进行新一轮进攻。

Large Action Models, as some call such LLMs, could change that.

大型行动模型（有些人这样称呼法学硕士）可能会改变这一现状。

LLMs will also allow digital twins to adapt.

LLM 还将允许数字孪生进行适应。

There are already some integrations of our technology into LLMs.

我们的技术已经与法学硕士进行了一些整合。

And LLMs just gave us a dramatic and unexpected example of that.

法学硕士给了我们一个戏剧性的、意想不到的例子。

That's not really relevant to LLMs.

这与LLMs并不真正相关。

Certainly, large language models (LLMs), of which the most famous is ChatGPT, produce what looks like impeccable human writing.

当然，大型语言模型，其中最著名的是ChatGPT，可以生成看起来无懈可击的人类文字作品。

But there's one thing that LLMs have that consistently impresses me, and that's these emergent abilities.

但法学硕士拥有的一件事一直让我印象深刻，那就是这些新兴能力。

They too argue that new rules are needed and that international cooperation on overseeing LLMs is essential.

他们也争辩说需要新的规则，并且在监督法学硕士方面的国际合作是必不可少的。

Perhaps, then, the problem is that accuracy is too much to ask of LLMs alone.

那么，问题可能在于，单单对法学硕士 (LLM) 的要求准确性太高了。

Even with the best algorithmic and architectural antipsychotics available, however, LLMs still hallucinate.

然而， 即使有了最好的算法和架构抗精神病药物，法学硕士仍然会产生幻觉。

We train LLMs on billions of web pages so they can produce textsthat's typical of what we humans write.

我们在数十亿个网页上培训法学硕士，以便他们能够生成人类所写的典型文本。

Many researchers believe the answer will come in training bigger LLMs on more and better data.

许多研究人员相信，答案在于利用更多更好的数据来训练更大的 LLM。

And indeed, LLMs seem to learn nested, hierarchical grammatical structures, even though they are exposed to only linear input, ie, strings of text.

事实上，虽然大型语言模型只接触了线性的输入内容，即文本字符串，但它们似乎学会了嵌套的层级语法结构。

One is that LLMs are not built to have perfect recall in the way a search engine or encyclopedia might.

一是 LLM 并不像搜索引擎或百科全书那样具有完美的回忆能力。