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Comment by insanitybit | Hacker News Reader

I just tried ChatGPT and it ran this code.

    from datetime import datetime, timedelta

    # Current date
    current_date = datetime(2023, 11, 1)
    
    # Calculate the date 140 days from now
    future_date = current_date + timedelta(days=140)
    future_date.strftime("%Y-%m-%d")

Result: '2024-03-20'

The ability to execute code is kinda insane for these models.

View on HN

Here’s the python ChatGPT-4 produced and ran with the title prompt:

# Number of elephants total_elephants = 10

# Elephants with legs elephants_with_legs = total_elephants - 2

# Number of legs for an elephant legs_per_elephant = 4

# Total legs total_legs = elephants_with_legs * legs_per_elephant total_legs

Its actual response was correct.

Yeah, ChatGPT gave me the correct answer without even needing Python in the case of the title.

Interesting, I copied the text into gpt 4 and it did run python as well.

Remembering that the correct answer is 40 because 'legless' is slang for 'drunk'?

Is that regional? I’ve never heard that euphemism.

Calling a drunk person legless is hardly an euphemism.

It is commonly used in British English. In fact, there is a famous comedy sketch about it that also uses the word 'caravaned'

It’s kind of funny that they can more reliably spit out code that will give an answer than actually output the answer as text. I guess it’s a workaround that works well for many cases

Humans can also more reliably use a calculator (which is basically what python is) for big numbers than doing it in their heads. I think it makes sense.

This reminds me, I've had an alias for calc='python -ic "from math import *' for a long time now. It comes handy more often than you'd think.

or octave if you need some real power

Doesn’t that just mean it was trained with inputs and outputs from simple programs?

No, it's actually executing that Python code. This is what allows an LLM (or an 'llm based system', I guess) to do something like "reverse <some uuid that has never been observed before>" - it can't just memorize the output and map it to the input because the output has literally never been observed. Instead, if it knows the algorithm for reversing a string, it can just use that and offload the execution to Python.

That is even less impressive. I was thinking—like normal linear models—it would be capable of interpolation.

It is. It doesn’t even need an existing language. You can define your own psuedo language in the prompt and have ChatGPT “execute” it (works best with 4 nonturbo).

You can even combine your pseudo language with natural language. See the OP’s custom GPT and the comments here: https://news.ycombinator.com/item?id=38594521

That looks totally different. In the case of the Python code it is literally executing it by calling out to CPython.

…got a source for that claim?

https://help.openai.com/en/articles/8302319-why-can-t-i-see-...

https://help.openai.com/en/articles/8437071-advanced-data-an...

I checked those links and didn’t see it mentioned that python code is actually executed. Could you quote the relevant part?

https://openai.com/blog/chatgpt-plugins#code-interpreter

>We provide our models with a working Python interpreter in a sandboxed, firewalled execution environment, along with some ephemeral disk space. Code run by our interpreter plugin is evaluated in a persistent session that is alive for the duration of a chat conversation (with an upper-bound timeout) and subsequent calls can build on top of each other. We support uploading files to the current conversation workspace and downloading the results of your work.

It really feels like I'm just googling for you, you had the feature name.

Why is it less impressive?

I would say creating a model which is able to interpolate from training data in a way which produces an accurate output of a new input is a little impressive (if only as a neat party trick), however anybody can run a python interpreter on a server somewhere.

I’m sure there are use cases for this. But in the end it is only a simple feature added onto a—sometimes—marginally related service.

Hm, I don't think of it that way I guess. What the LLM is doing is generalizing a problem based on previous problems it has seen and then offloading the execution of that problem to a machine with some defined, specific semantics.

This is a lot more than a party trick. The model is able to describe the program it wants to execute and now it can accurately execute that - that it 'offloads' the work to a specialized program seems fine to me.

It's way more than a simple feature, this is enabling it to overcome one of the biggest limitations and criticisms of LLMs - it can answer questions it has never seen before.

There a code interpreter built in: https://www.pluralsight.com/resources/blog/data/chatgpt-code...

So it only works if the code is 100% correct. Which according to my experience doesn't happen often.

Sure, don’t expect perfection, but it still works great.

Most of the time it's just doing really simple stuff like this.

It even correctly adjusted for the leap year.

But is that actually output from a LLM, or is ChatGPT just running the Python code through a Python interpreter?

Interpreter.

Still not worth $20/mo

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