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The Inherent Limitations of GPT-3
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Editorials
The Inherent Limitations of GPT-3
On why GPT-3 as is will not cost many people their jobs or soon lead
to AGI
Nov 27
2
Welcome to the seventh editorial from Last Week in AI!
Our editorials are typically a perk for paying subscribers, but on
account of this being the week of Thanksgiving we are releasing this
one for free. We hope you will consider subscribing to our substack
to get access to future ones and to support us, and you can do so
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In my last editorial, I went over the fact that GPT-3 was a big deal
and caused a large stir in the world of AI. Some thought it such a
big deal as to worry about losing jobs and career paths in a post
GPT-3 world, and many thought it to be a major leap towards the goal
of AGI. But, as Skynet Today covered at the time of its release, much
of the hype surrounding GPT-3 was excessive and overestimated its
capabilities. This was not a novel position; OpenAI's CEO at the time
said as much:
[https]
Others have already pointed out the various limitations of GPT-3 that
mean people may not need to worry so, and my aim with this piece is
to recap and explain these limitations more fully and succinctly than
other articles have. These limitations may of course be addressed in
future iterations of GPT, but none are trivial -- and some are very
challenging -- to fix. They also apply to any models similar to
GPT-3, and I only address GPT-3 in particular as it is the most well
known and most discussed instance of such models. With that being
said, let us go on to its list of limitations.
First, and most obviously, GPT-3 deals only with text. After all, it
is a language model. The most exciting aspect of it is that it proved
to be able to do a large variety of tasks that involve text. But text
alone is clearly not that general; a huge part of what humans do
involves images, video, audio, and other sorts of data. And as we
shall see, GPT-3 has further limitations that limit many things it
can do with text, as well.
[https]An example of GPT-3 in action (source)
The next most obvious and most significant limitation is that GPT-3
has limited input and output sizes. It can take in and output 2048
linguistic tokens, or about 1500 words. That's a substantial number
of words and more than past iterations of GPT, but still quite
limited. There are workarounds for this, but research on it is still
in its early stages. Likewise, GPT-3 lacks any form of memory. In
other words, it cannot remember inputs it has seen or outputs it has
produced in the past.
These past two limitations already demonstrate that GPT-3 inherently
cannot do many text-related tasks. Much has been said about its
potential to put many programmers out of a job, but such jobs
typically require knowing a large amount of context with respect to
the need and goal of the project. With its limited input-size and
output-size, GPT-3 would not be able to absorb all this context or
output code that leverages this context without human aid. And with
its lack of memory, GPT-3 would not be able to take part in iterative
development that requires awareness of past developments.
Of course, the job of programming will likely evolve with the
introduction of the GPT-3 based GitHub CoPilot, but this is a long
way from costing people their jobs. Many other examples of tasks that
are likewise made impossible (or at least hard) by these limitations
exist: writing novels or other long documents, engaging in ongoing
conversations, understanding long email threads, etc.
Next, we get to a subtler but still important limitation: GPT-3 lacks
reliability and interpretability. That is, it can be hard to
guarantee that its outputs will always be acceptable, and hard to
know why it is the case when its outputs are not acceptable. This
makes it unusable in contexts where even an incorrect output in 1
case out of a 100 is unacceptable. The GPT-3 paper makes this point
as well:
"GPT-3 shares some limitations common to most deep learning systems -
its decisions are not easily interpretable, it is not necessarily
well-calibrated in its predictions on novel inputs as observed by the
much higher variance in performance than humans on standard
benchmarks, and it retains the biases of the data it has been trained
on. This last issue - biases in the data that may lead the model to
generate stereotyped or prejudiced content - is of special concern
from a societal perspective, and will be discussed along with other
issues in the next section on Broader Impacts"
Again, much research is focused on addressing this issue, and again,
it is still in its early stages. Biases aside, it can often output
things that are only subtly flawed or untrue, which is even worse.
Again going back to the case of programmers, an essential part of the
job is debugging, and this may be made harder when these bugs are
introduced by an AI. Even writing essays with GPT-3 is a questionable
idea, as it can often output text that is plausible but also utterly
incorrect:
[https]A quite incorrect GPT-3 based autocompletion I generated using
SudoWrite
On to a more minor but still important limitation: GPT-3 is slow to
both run and train. Its sheer size makes it so it can take a second
or more for it to produce an output. This is not an issue for all
applications, but it is for many. Again, this is noted in the GPT-3
paper:
"A limitation associated with models at the scale of GPT-3,
regardless of objective function or algorithm, is that they are both
expensive and inconvenient to perform inference on, which may present
a challenge for practical applicability of models of this scale in
their current form."
Moreover, training it can take weeks or months even with powerful
computing clusters. A crucial aspect of using machine learning models
in production is to do so in an iterative manner with continuous
re-training of the model, which is of course hard to do if training
takes that long.
Lastly, GPT-3 has a host of other technical limitations, many of
which are outlined in the paper:
"GPT-3 has several structural and algorithmic limitations
...
A more fundamental limitation of the general approach described in
this paper - scaling up any LM-like model, whether autoregressive or
bidirectional - is that it may eventually run into (or could already
be running into) the limits of the pretraining objective.
...
Another limitation broadly shared by language models is poor sample
efficiency during pre-training. While GPT-3 takes a step towards
test-time sample efficiency closer to that of humans (one-shot or
zero-shot), it still sees much more text during pre-training than a
human sees in their lifetime."
To summarize, GPT-3 has many limitations that constrain its
capabilities, and these limitations are important to be aware of when
predicting its potential impact on your career or the future in
general. That is not to say that GPT-3 is not a major milestone in AI
research -- in my opinion it very much is. It has already led to the
formation of many startups as well as GitHub CoPilot, and so its
impact will likely be considerable. As with any major scientific
development, it's great to be excited about as long as you don't get
carried away and recognize there is still a long and winding road
ahead, with this development being but a step on that road.
Share
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About the Author:
Andrey Kurenkov (@andrey_kurenkov) is a PhD student with the Stanford
Vision and Learning Lab working on learning techniques for robotic
manipulation and search. He is advised by Silvio Savarese and
Jeannette Bohg.
2
[https]
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