ChatGPT与乔姆斯基: 人类如何习得语言(上)

Culture

文艺版块

Johnson

约翰逊专栏

The language instinct

语言本能

ChatGPT's way with words raises questions about how humans acquire language.

ChatGPT运用语言的方式引发了关于人类如何习得语言的疑问。

When deep blue, a chess computer, defeated Garry Kasparov, a world champion, in 1997 many gasped in fear of machines triumphing over mankind.

1997年，当国际象棋计算机深蓝击败世界冠军加里·卡斯帕罗夫时，许多人因害怕机器战胜人类而发出惊呼。

In the intervening years, artificial intelligence has done some astonishing things, but none has managed to capture the public imagination in quite the same way.

在这些年间，人工智能完成了一些惊人之举，但都没能像深蓝那样激发公众的想象。

Now, though, the astonishment of the Deep Blue moment is back, because computers are employing something that humans consider their defining ability: language.

但现在深蓝时刻带来的惊喜又回来了，因为计算机正在运用一种被人类视为是定义了其本质的能力：语言。

Or are they?

真是这样吗？

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 a debate has ensued about what the machines are actually doing internally, what it is that humans, in turn, do when they speak--and, inside the academy, about the theories of the world's most famous linguist, Noam Chomsky.

但紧接着出现了一场辩论：机器内部究竟发生了什么？人类在说话时又发生了什么？在学术界，学者们也在就世界上最著名的语言学家诺姆·乔姆斯基的理论展开讨论。

Although Professor Chomsky's ideas have changed considerably since he rose to prominence in the 1950s, several elements have remained fairly constant.

虽然乔姆斯基教授自20世纪50年代崭露头角以来，他的思想已经发生了很大的变化，但有几个要素仍然相当稳定。

He and his followers argue that human language is different in kind (not just degree of expressiveness) from all other kinds of communication.

他和其追随者认为，人类语言与所有其他种类的交流方式在种类上(而不仅仅是表达程度上)是不同的。

All human languages are more similar to each other than they are to, say, whale song or computer code.

各种人类语言彼此之间的相似性都高于人类语言和鲸鱼的歌声或计算机代码之间的相似性。

Professor Chomsky has frequently said a Martian visitor would conclude that all humans speak the same language, with surface variation.

乔姆斯基教授经常说，一个来自火星的访客会得出结论：所有人类都说同样的语言，只是表面上有所不同。

Perhaps most notably, Chomskyan theories hold that children learn their native languages with astonishing speed and ease despite "the poverty of the stimulus": the sloppy and occasional language they hear in childhood.

或许最值得注意的是，乔姆斯基的理论认为，尽管儿童在童年时只偶尔听到一些零碎语句，因而"语言刺激贫瘠"，但儿童能以惊人的速度轻而易举地学会母语。

The only explanation for this can be that some kind of predisposition for language is built into the human brain.

对此唯一的解释可能是人类大脑中天生就有某种语言倾向。

Chomskyan ideas have dominated the linguistic field of syntax since their birth.

乔姆斯基的思想从其诞生之日起就一直统治着语言学的句法领域。

But many linguists are strident anti-Chomskyans.

但许多语言学家是尖锐的反乔姆斯基主义者。

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。

Grammar has a hierarchical, nested structure involving units within other units.

语法有嵌套的层级结构，语言单位当中又有其他语言单位。

Words form phrases, which form clauses, which form sentences and so on.

词构成短语，短语构成子句，子句构成句子，如此等等。

Chomskyan theory posits a mental operation, "Merge", which glues smaller units together to form larger ones that can then be operated on further (and so on).

乔姆斯基理论假定了一种心理操作"合并"，即将较小的单元粘合在一起形成较大的单元，然后对较大的单元再进行这种操作（如此等等）。

In a recent New York Times op-ed, the man himself (now 94) and two co-authors said "we know" that computers do not think or use language as humans do, referring implicitly to this kind of cognition.

在《纽约时报》最近的一篇社论文章中，乔姆斯基本人(现年94岁)和两位合著者说，"我们知道"计算机思考或使用语言的方式和人类不一样，暗指的就是人类的这种认知方式。

LLMs, in effect, merely predict the next word in a string of words.

而大型语言模型实际上只是预测一长串单词中的下一个单词是什么。