How will AC deal with the case when a reviewer shows up in the last 2 hours to ddl and posts some new questions? Though we responded soon, the reviewer didn't reply anymore...

I’ve heard the RLHF change explained as actually a good thing, though. Here’s an example:

Say you ask it a question to which it assigns 90% probability to answer X and 10% probability to answer Y. Base GPT-4 gives the answers in these proportions: 90% of the time it says X and 10% of the time it says Y.

But if it’s 90% sure the answer is X, you don’t want it to say Y is the answer at all, even 10% of the time! It’s better for it to always say X. (Though the best may be to give a thorough account of its respective probability assessments.) So RLHF improves the behavior of the model by uncalibrating the rate of responses from their probabilities.

Yea, but what's it's confidence score for it's confidence score calculation? /s

Sounds more like you're asking about digital make-up, which can range from instagram filters to virtual avatars. And yeah, we can't tell how much of their presented look is real without a reference.

But does it matter? These people create an identity that only exists in the digital world. Who cares what they look like in the real world if you're never going to see them in the real world?

In my mind this + plugins + modules (vision) is the next step. Am I crazy?

> The problem with your solution is that it probably biases the model towards making up some papers just to fit the prompt and have a mix.

That's a very important point, adding an extra condition (if 'p' then 'q') to the prompt makes the model biased towards doing 'p' then doing 'q' to fulfil the prompt despite the condition still being met if it just avoided doing 'p'.

For a more concrete example, here's me asking ChatGPT to write two essays:

1- Write a paragraph about zoos. Figure. (Notice how no Elephants are mentioned)

2- Write a paragraph about zoos with (if 'p' then 'q') condition. Figure (Notice how only this answer mentions Elephants)

Well, our own body is alien to us. The brain, the gut, the endocrine system, and so on. There are emergent complexities everywhere from giant black holes to a pile of dirt. It is the same with conceptual things like math or computer science. Simple axioms and logic gates lead to beautiful complex systems.

I guess, we should get used to "not understanding" at this point.

Who needs statistical tests with theoretical grounding and justified/repeatable results when you've got LLMs™

But we don't know if the text output actually gives us access to those confidences or if it is just making them up, do we?

Just let it give you the dois as a list and a script to verify their existence

But isnt it still just simulating a text of fact checking instead of fact checking it?

Also if the second neural network is running as a separate internet-connected application, it can go out and verify the output of the first, send back its results, and tell the first to either change or remove each paper that it cannot find and verify. The second neural network can make errors as well, but through these interconnected systems errors can be reduced somewhat largely.

Keeping a second GPT-4 window open and asking it to verify information from the first seems to work pretty well. The models fail by guessing when uncertain, but they have no incentive to cooperate and back up one another's guesses. The problem with your solution is that it probably biases the model towards making up some papers just to fit the prompt and have a mix.

inb4 this post is part of the training set for the next generation of LLMs along with the comments, sarcasm and what not

Exactly, a lot of the tech is already there, it's perhaps more about unobfuscating the code rather than decompiling it.

So this is an Open domain hallucination:

​

Closed domain hallucinations refer to instances in which the model is instructed to use only information provided

in a given context, but then makes up extra information that was not in that context. For example, if you ask the

model to summarize an article and its summary includes information that was not in the article, then that would be a

closed-domain hallucination.

Open domain hallucinations, in contrast, are when the model confidently provides false

information about the world without reference to any particular input context.

​

​

They handled this via : For tackling open-domain hallucinations, we
collect real-world ChatGPT data that has been flagged by users as being not factual, and collect
additional labeled comparison data that we use to train our reward models.

​

​

Not very productive. The best way to check references would be using a plugin and instructions to the model to "check references". The machine also needs to have RL training so that it will use the plugin and use it correctly the first time.

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That depends on the language/compiler used. Java and C# have decompilers that turn out great code.

I think that if you can have better variable names that is already a big selling point

Friends working in rev.ng told me that it's very difficult to decompile to the original high level structures actually used in the source code. Maybe C have a few ways to code a loop but c++ has many and figuring out the source code from assembly is very hard to achieve with rule based systems.

How does a Transformer architecture handle inputs of different lengths? Is the sequence length limit inherent to the model architecture or more because of resource issues like memory?

Try having it generate tests first. You gotta get it into the proper context.

You could do that, but if it's just hallucinating the confidence intervals then it really isn't very neat. The language model have very high reward for hallucinated responses for things like confidence intervals in particular, because hallucinating figures like this will still produce very coherent responses.

It can in fact think in steps. All you have to do is ask it to. In fact, multiple papers have shown that asking it to think in steps provides a significant increase in the accuracy of it's answers.

that said, it might be also a good approach to preprocess the input with a classical algorithm and then train a model on refactoring that decompiled code, so it becomes more readable