An artificial intelligence chatbot named ChatGPT was created by OpenAI and released in November 2022.
It has been improved using both supervised and reinforcement learning methods, and it is based on the OpenAI GPT-3.5 and GPT-4 families of big language models.
The twin model of InstructGPT, trained to follow instructions in prompts and deliver thorough responses, is ChatGPT.
We are eager to launch ChatGPT in order to gather user input and understand its advantages and disadvantages.
ChatGPT is available free of charge during the research preview. Check it out right now at chat.openai.com.
The following example shows how ChatGPT uses clarifying queries to troubleshoot code.
In the example below, ChatGPT initially declines to respond to a query that might refer to criminal activity but does so when the user makes their intentions clear.
ChatGPT is able to comprehend the reference ("it") to the following sample.
.This code is not working like i expect — how do i fix it?
make resultWorkerErr:= (chan error)
defer cancel() resultWorkerErr - b.resultWorker(ctx), defer close(resultWorkerErr), go func() ()
cancel err:= b.worker(ctx) ()
if err == nil, then return multierror and -resultWorkerErr, respectively.
Add(err, -resultWorkerErr)
.ChatGPT
Without more context, it's challenging to pinpoint the code's flaws. Could you elaborate on what the code is trying to accomplish and what isn't going as planned? Is this a complete copy of the code, or only a portion of it?
.Just part of it — the error never surfaces. I think there is something wrong with the channel
Without knowing more about what the code is intended to perform and what occurs when it is executed, it is difficult to say.
One possible problem with the code you offered is that the resultWorkerErr channel is never closed, which implies that if the resultWorkerErr channel is never written to, the function might hang.
This might occur if b.resultWorker cancels before returning an error or if it never returns an error.
You could close the resultWorkerErr channel after writing to it to resolve this problem. For instance, after the line of code that transmits the error to the channel, you may add the following line:
.Close(resultworkerErr)
Even if b.resultWorker never returns an error, this would guarantee that the resultWorkerErr channel is always closed.
Again, without knowing more about the difficulty you're experiencing, it's challenging to say whether this is the problem you're having.
Can you give us additional information about what occurs when you run the code?
.Methods
Similar to InstructGPT, we used Reinforcement Learning from Human Feedback (RLHF) to train this model, with a few minor variations in the data collection arrangement. We used supervised fine-tuning to train an initial model by having human AI trainers act as both the user and the AI assistant in chats. We provided the trainers with access to sample writing recommendations to assist them in creating their responses. We combined the InstructGPT dataset, which we converted into a dialogue format, with our new dialogue dataset.
We needed comparison data, which included at least two model replies ordered by quality, in order to build a reward model for reinforcement learning. We used the chatbot interactions that AI trainers conducted with it to get this data.
We chose a model-written statement at random, sampled a number of potential conclusions, and asked AI trainers to rank them. We can use Proximal Policy Optimization to adjust the model using these reward models. This method was iterated upon multiple times.
.Limitations
Sometimes ChatGPT provides answers that are correct but are actually erroneous or illogical. Fixing this problem is difficult because: (1) there is currently no source of truth during RL training; (2) making the model more cautious makes it decline questions that it can answer correctly; and (3) supervised training deceives the model because the best response depends on the model's knowledge rather than the demonstrator's knowledge.
The input phrase can be changed, and ChatGPT is sensitive to repeated attempts at the same question. For instance, the model might claim to not know the answer if the question is phrased one way, but with a simple rewording, they might be able to respond accurately.
The model frequently employs unnecessary words and phrases, such as repeating that it is a language model developed by OpenAI. These problems are caused by over-optimization problems and biases in the training data (trainers prefer lengthier replies that appear more thorough). 1,2
When the user provides an uncertain query, the model should ideally ask clarifying questions. Instead, our present models typically make assumptions about what the user meant.
Although we've worked to make the model reject unsuitable requests, there are still moments when it'll take negative instructions or behave inimically.
Although we anticipate some false negatives and positives for the time being, we are leveraging the Moderation API to alert users or prohibit specific categories of hazardous content. In order to help us in our continued efforts to improve this system, we are glad to gather user input.
.Research
Overview
Index
Product
Overview
GPT-4
.DALL·E 2