2023-04-10 17:10:44
Simply explained: how does GPT work?
March 4, 2023 | Translations:
Fr
By now, you have probably heard of OpenAI s ChatGPT, or any of the alternatives
GPT-3, GPT-4, Microsoft s Bing Chat, Facebook s LLaMa or even Google s Bard.
They are artificial intelligence programs that can participate in a
conversation. Impressively smart, they can easily be mistaken for humans, and
are skilled in a variety of tasks, from writing a dissertation to the creation
of a website.
How can a computer hold such a conversation? Let s have a look at how it works.
A probabilistic approach
The simplest model for a natural language is a naive probabilistic model, also
known as a Markov chain1. The idea is simple: take a reference text, the
longer, the better, and learn the probabilities of word sequences. For
instance, given the sentence:
The cat eats the rat.
The model will learn that after cat there is always eats , then the . But
after the , there is a 50% chance of having cat and 50% chance of having
rat . We can use this model to ask what s the next word after an incomplete
sentence. If we repeat this process, we can generate entire sentences.
The model
If we ask the model to generate a sentence, we could get exactly the same thing
as the training text:
The cat eats the rat.
We could also get:
The rat.
The cat eats the cat eats the cat eats the rat.
Every time we reach the word the , the model can choose between rat or cat
.
Of course, the text we ll use to train the model will be much longer, but you
can already see some of the issues. If we train it on the entire Wikipedia
website, we could get something like:
Explaining his actions, and was admitted to psychiatric hospitals because of
Davis s strong language and culture.2
The sentence is more complex, the vocabulary richer, but it doesn t make any
sense because the model is lacking context: it s only using the latest word to
generate the next one. We could extend the model to take into account 2, 3 or 4
context words ( eats the is followed by rat ), but then we might just be
repeating entire sections of the input text: How many times are there the exact
same sequence of 4 words on Wikipedia?
From words to meaning
So far, one of the problems was that we were treating words as a bunch of
letters without meaning. The model doesn t understand the relationship between
the and a , between king and queen , etc. How can we extract meaning from
the words? To try to explain the meaning and define the words to a computer is
a dead end, the task is way too complex (people have been trying for decades).
How can you even represent the meaning of a word? Well, there is one thing that
computers understand perfectly: numbers. What if we represented the meaning of
words as numbers along several axes?
For instance: on a scale of -1 (masculine) to 1 (feminine), how do you evaluate
this word?
king: -1
queen: 1
table: 0
mustache: -0.9
Or: on a scale of -1 (mean) to 1 (nice), how do you evaluate this word?
wolf: -0.8
princess: 0.9
table: 0.1
gift: 1
Or even: on a scale of -1 (noun) to 1 (verb), how do you evaluate this word?
king: -1
speak: 1
pretty: 0
And so on. With enough axes on which to evaluate words, we should be able to
get an approximation of the meaning of a word. The problem becomes: How do you
pick the axes, and how do you evaluate all the words? Once more, the task is so
complex that we ll let the computer do the hard work: we ll just tell it that
words that appear together have a related meaning. With enough text, the
computer can determine the axes and their evaluation. In our cat example, both
the cat and the rat are animals (close meanings), and it s useful to know that
eats is something that animals do. But in a maths textbook, there will be no
cat or rat, because their meaning is far from the words used in the textbook.
The axes we get are often hard to explain: We might find some expected axes
like masculine/feminine, but most will be more complex, either having meaning
only when combined with other axes or representing several concepts at once.
This method is known as word embedding , representing words as a vector of
numbers.
From meaning to relationships
Now that we have our meaning as numbers, we can use interesting properties: we
can add them for instance. What does it mean? Well, for instance adding USA
and currency (or rather adding their numerical representations) will yield
dollar (or rather numbers that are close to the numerical representation of
dollar ). USA + capital = Washington , eat + noun = meal , and so on.
We can also subtract: for instance king - man + woman = queen , or
Washington - USA + England = London .
We can also use it to find closely related words, synonyms.
Can we learn these relationships?
By using this numerical word representation, we can go back to our initial
model, but this time learning relationships rather than words. However, since
relationships are more complex, we ll need more context. Thankfully, now that
we have numbers, we can use approximations. Instead of learning after cat ,
there s eats , we can learn relationships like: after an article and a noun,
there is often a verb , animals often eat, drink and run , rats are smaller
than cats , and you can only eat smaller things than you . Everything
expressed in numbers, of course.
These relationships are complex, so we ll need a lot of text to train the
model. They are represented as an equation: think $y = a \cdot x_1 + b \cdot
x_2 + c$, but with more inputs (different $x_1$) and parameters ($a$, $b$ and
$c$). Now, instead of following probabilities from word to word, there is an
equation for each axis (like masculine/feminine). In total the model has
hundreds of billions, even trillions of parameters! This allows it to take into
account a larger context:
20 words would allow it build simple sentence with a correct structure.
100 words would allow it to develop a simple idea over a small paragraph.
With a thousand words, it could have a conversation without losing track.
The biggest models have in the order of 20000 words, which allows them to read
an entire article, a short story, or have a long conversation while still
considering the whole context before generating the next word.
In the end, everything is a question of size: a bigger model can learn more
relationships, and take into account more context.
What are the strengths and weaknesses of GPT?
GPT is skilled at generating text that looks like it was written by a human. It
is able to link ideas logically, defend them, adapt to the context, roleplay,
and (especially the latest GPT-4) avoid contradicting itself.
Unfortunately, it is likely to lie, or rather to let its imagination run wild
in the absence of data. Asking for the result of a mathematical problem is
running the risk of getting an approximate if not outright false answer. Given
that the training data stops in September 2021, it will invent things when
asked about current matters. In order to avoid this, Bing Chat and Google Bard
connect the model to a search engine (Bing or Google) to let it request
up-to-date information.
In order to use GPT productively, it is essential to apply it to tasks that are
either fuzzy and error-tolerant (generate a marketing email?), or easily
verifiable, either by a (non-AI!) program or by a human in the loop.
Can the model think?
The first answer, now that we know how it works, is no: the model is a
glorified mathematical equation that generates next word probabilities.
However, it s worth considering our own brain: We have a network of neurons
(100 billions) connected to each other (10 000 connections per neuron),
reacting to context, learning from experience, and generating an appropriate
(but often hard to predict exactly) answer. In other words, apart from the fact
that our algorithm is chemical rather than digital, the structure is similar.
What are the differences then?
3 orders of magnitude in complexity: The human brain has 1000 times more
connections than GPT-4 has parameters. As a result, it can handle more complex
situations.
Ongoing learning: The brain keeps learning, including during a conversation,
whereas GPT has finished its training long before the start of the
conversation.
Limited to words: The GPT interface is limited to words. However, as we saw,
there is a semantic system inside, that is only transformed back into words in
the last step. It is conceivable to train such a model to control a robot
(given enough training data).
Limited input: The only thing GPT knows about the conversation is the text. Up
to 60% of human communication is nonverbal: the tone of voice, the rhythm of
the voice, the facial expression, even some subconscious factors like smell
play a part. GPT misses all of that.
Other differences we could mention are at the behavior level:
GPT has trouble applying logical rules consistently, it is more a best effort
scenario. Ironically, it doesn t know how to do maths. But this is comparable
to a small child.
GPT doesn t have emotions: Human emotions involve a lot of glands and hormones
that have complex interactions with the brain. However, GPT learned the
behaviors associated with emotional states from conversations between humans.
It is able to behave as if it had emotions, does that count for anything? Some
conversation transcripts show that GPT act as if it is aware that it is a
program, and sometimes asks existential questions.
You could argue that GPT is not conscious. The definition of consciousness has
often evolved and depends on the person, but it is often defined in such a way
that only humans qualify for consciousness. If a program acts in a way that is
indistinguishable from a human, would we agree that it is conscious? The
Chinese Room argument3 holds the contrary: If it s possible to pass for a
Chinese speaker by following instructions without understanding Chinese
yourself, it means that a computer who is just following its program doesn t
understand Chinese, and by extension is not conscious.
What are the consequences for society?
I cannot predict the future, especially at the dawn of a revolutionary
technology, but know this: it is a revolutionary technology. For many knowledge
workers, from marketing to engineer, from recruiters to social workers, GPT
will change things. In the same way that the assembly line changed the craftman
s job, that the calculators and computers changed accounting, that mass media
changed politics, GPT will change the world of the knowledge worker.
Granted, all these jobs will not disappear overnight: We still have craftsmen
and accountants, but where you once needed a team of 10 people in your
marketing department, maybe one or two employees equipped with GPT can fill the
role.
As for a lot of scientific or industrial progress, this change will affect many
people: some will have to change careers or learn to integrate GPT in their
profession; others will lose their job. New positions will be created by GPT
either directly (like the Prompt Engineer, the one who can talk to the machine
) or indirectly by making it easier to create products and companies.
It is difficult to know the exact consequences, but we are at the beginning of
a new phase where many things will change, where people with technical skills
are at an advantage, and where entrepreneurs have a brand-new field of
opportunity. On the other hand, many people who are not ready to change, who do
not have the skills or who cannot afford to reeducate are threatened.
Will GPT destroy society?
Some people fear the end of the world because of AI: from Matrix to Terminator,
it s a common trope in dystopian science fiction. In general, the two scenarios
are:
The Terminator scenario: The AI is built to win a war and given access to
military resources, maybe by a dictator, and is granted a survival instinct.
Humans try to stop it, and viewing that as a threat, the AI reacts violently.
The Paperclip Optimizer: In this parable, the AI is tasked with creating as
many paperclips as possible. Having exhausted the available resources on the
planet, it turns to the next most available carbon source: humans. Another
version sees the humans try to stop the machine; the AI realizes that to build
paperclips in peace it must get rid of the humans. It is like the evil genie
who twists your wish by giving you exactly what you asked, instead of what you
really wanted.
One thing to realize is that (for now) GPT can only produce text. Of course,
mere text in the wrong hands can be dangerous (after all, a dictator just
talks ), but on its own, GPT cannot do anything. However, it can be the first
step towards a more capable system: a derivative of GPT put in control of a
robot, a military decision assistant, etc.
We will need to proceed with caution, and step in if progress turns out to be
uncontrollable, or at least uncontrolled.
On a positive note, some AI experts actively research ways to guard against
these scenarios, so there may be some safe ways forward.
1 That model was used in the first keyboard suggestions on iPhones:
completing words, or predicting the next word. ↩︎
2 https://medium.com/@inzaniak/markov-chains-on-wikipedia-98d472421719
3 Chinese Room argument on Wikipedia ↩︎