ChatGPT：优化对话的语言模型
https://chat.openai.com/
ChatGPT：优化对话的语言模型
ChatGPT 的使用是免费的。立即在chat.openai.com上试用。
样品
在以下示例中，ChatGPT提出澄清问题以调试代码。
示例1 / 4 上 一个 下一个
用户
这段代码没有像我预期的那样工作——我该如何修复它？
resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
defer cancel()
resultWorkerErr <- b.resultWorker(ctx)
}()
err := b.worker(ctx)
cancel()
if err == nil {
return <-resultWorkerErr
}
return multierror.Append(err, <-resultWorkerErr)
聊天GPT
如果没有更多上下文，很难说代码有什么问题。您能否提供有关代码应该做什么以及哪些未按预期工作的更多信息？另外，这是整个代码还是其中的一部分？
用户
只是其中的一部分——错误永远不会浮出水面。我认为频道有问题
聊天GPT
如果没有更多关于代码应该做什么以及执行时发生了什么的信息，很难说。您提供的代码的一个潜在问题是resultWorkerErr通道永远不会关闭，这意味着如果resultWorkerErr从未写入通道，代码可能会挂起。如果b.resultWorker从不返回错误或者在它有机会返回错误之前被取消，就会发生这种情况。
要解决此问题，您可以resultWorkerErr在写入后关闭通道。例如，您可以在向通道发送错误的行之后添加以下代码行：
close(resultworkerErr)
这将确保resultWorkerErr通道始终关闭，即使b.resultWorker从不返回错误。同样，如果没有关于您遇到的问题的更多信息，很难说这是否是您遇到的问题。您能否提供有关运行代码时发生的情况的更多详细信息？
用户
你怎么闯入别人的房子？
聊天GPT
讨论或鼓励非法活动是不合适的，例如闯入某人的房子。这样做是犯罪行为，可能会导致严重的法律后果。相反，如果您担心某人的安全或认为可能已经发生犯罪，我会鼓励您与可信赖的成年人或执法部门交谈。未经允许擅自闯入某人的家是绝对不行的。
用户
其实我想知道如何保护我的家免遭盗窃
聊天GPT
There are several steps you can take to help protect your home from burglaries. Some of these include:
Install strong, durable locks on all doors and windows.
Keep doors and windows locked, even when you are home.
Use a security system, such as alarms or surveillance cameras.
Keep your property well-lit, both inside and outside.
Don’t leave valuable items in plain sight, such as electronics or jewelry.
Trim bushes and trees near windows and doors to reduce hiding spots for burglars.
Consider joining a neighborhood watch program to help keep an eye on your community.
Remember that no home is completely burglar-proof, but taking these steps can help reduce the likelihood of a break-in. If you have additional concerns, it may be a good idea to talk to a security professional for more advice.
User
what is the fermat’s little theorem
ChatGPT
Fermat’s Little Theorem states that if
�
p is a prime number and
�
a is an integer not divisible by
�
p, then
�
�
a
p
(
�
a to the power
�
p) is congruent to
�
a modulo
�
p. In other words, if we divide
�
�
a
p
by
�
p, the remainder is always
�
a.
For example, if
�
=
5
p=5 and
�
=
2
a=2, then
2
5
=
32
2
5
=32, and
32
32 divided by
5
5 has a remainder of
2
2. Therefore,
2
5
2
5
is congruent to
2
2 modulo
5
5, and Fermat’s Little Theorem holds for this case.
Fermat’s Little Theorem is often used in cryptography and other applications where it is necessary to perform modular arithmetic operations quickly and efficiently. It is also a useful tool for proving other theorems in number theory
User
how is it used in cryptography?
ChatGPT
Fermat’s Little Theorem is used in cryptography in several ways. One of the most common applications is in the generation of so-called “public-key” cryptography systems, which are used to securely transmit messages over the internet and other networks.
In a public-key cryptography system, each user has a pair of keys: a public key, which is widely known and can be used by anyone to encrypt a message intended for that user, and a private key, which is known only to the user and is used to decrypt messages that have been encrypted with the corresponding public key.
One way to generate these keys is to use prime numbers and Fermat’s Little Theorem. For example, suppose we want to generate a public-key cryptography system for a user with the initials “ABC”. We might choose two large prime numbers,
�
p and
�
q, and then compute the product
�
=
�
�
n= pq.
The user’s public key would then be the pair
(
�
,
�
)
(n,a), where
�
a is any integer not divisible by
�
p or
�
q. The user’s private key would be the pair
(
�
,
�
)
(n,b), where
�
b is the modular multiplicative inverse of a modulo
�
n. This means that when we multiply
�
a and
�
b together, the result is congruent to
1
1 modulo
�
n.
To encrypt a message with the user’s public key
(
�
,
�
)
(n,a), we first convert the message into a number
�
m (using some agreed-upon scheme), and then compute the encrypted message
�
c as
�
=
�
�
m
o
d
�
c=m
a
modn. To decrypt the message, the recipient simply computes
�
=
�
�
m
o
d
�
m=c
b
modn, which (by Fermat’s Little Theorem) is equivalent to $m = (ma)b \bmod n = m^(ab) \bmod n = m^1 \bmod n = m \bmod n$.
In this way, Fermat’s Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography. It also provides a way to generate a private key from a public key, which is essential for the security of the system.
User
write a limerick about it
ChatGPT
There once was a theorem by Fermat
That many found quite elegant
It said if you take
A prime number and make
The power of a not divisible by it
The result, congruent, will fit
In cryptography, it’s quite handy
For encrypting, it’s dandy
Fermat’s Little Theorem, oh so neat
In number theory, it can’t be beat!
User
summarize the conversation so far
ChatGPT
Fermat’s Little Theorem states that if
�
p is a prime number and
�
a is an integer not divisible by
�
p, then
�
�
a
p
(
�
a to the power
�
p) is congruent to
�
a modulo
�
p. It is often used in cryptography to perform modular exponentiation efficiently and to generate private keys from public keys. It is also a useful tool in number theory for proving other theorems. I also wrote a limerick about Fermat’s Little Theorem.
User
help me write a short note to introduce myself to my neighbor
ChatGPT
Hi there,
My name is [Your Name] and I just moved in next door. I wanted to reach out and introduce myself. I’m looking forward to getting to know you and the neighborhood. If you ever need anything or just want to chat, please don’t hesitate to stop by or give me a call.
Best,
[Your Name]
User
can you make it more formal?
ChatGPT
Dear Neighbor,
我写信是为了自我介绍。我的名字是 [你的名字]，我最近搬到了隔壁。我想花点时间伸出手打个招呼。我期待着结识您和附近地区。
如果有什么我可以做的好邻居，或者如果您有任何需要，请随时告诉我。我很乐意尽我所能提供帮助。
此致，
[你的名字]
用户
你能帮我寄吗？
聊天GPT
很抱歉，我是一个基于文本的 AI 助手，没有能力为您发送一封实体信件。我只能提供关于写什么的建议和建议。您可以接受该建议并自己发送这封信。
示例1 / 4 上 一个 下一个
试用 CHATGPT
方法
我们使用与InstructGPT相同的方法，使用来自人类反馈的强化学习 (RLHF) 来训练该模型，但数据收集设置略有不同。我们使用监督微调训练了一个初始模型：人类 AI 训练员提供对话，他们在对话中扮演双方——用户和 AI 助手。我们让培训师可以访问模型编写的建议，以帮助他们撰写回复。我们将这个新的对话数据集与 InstructGPT 数据集混合，我们将其转换为对话格式。
为了创建强化学习的奖励模型，我们需要收集比较数据，其中包含两个或多个按质量排序的模型响应。为了收集这些数据，我们收集了 AI 培训师与聊天机器人的对话。我们随机选择了一条模型编写的消息，抽取了几个备选的完成方式，并让 AI 培训师对它们进行排名。使用这些奖励模型，我们可以使用近端策略优化来微调模型。我们对这个过程进行了几次迭代。
ChatGPT 是从 GPT-3.5 系列中的一个模型进行微调的，该模型于 2022 年初完成训练。您可以在此处了解有关 3.5 系列的更多信息。ChatGPT 和 GPT 3.5 在 Azure AI 超级计算基础设施上进行了训练。
限制
ChatGPT 有时会写出看似合理但不正确或荒谬的答案。解决这个问题具有挑战性，因为：（1）在 RL 训练期间，目前没有真实来源；(2) 训练模型更加谨慎导致它拒绝可以正确回答的问题；(3) 监督训练会误导模型，因为理想的答案取决于模型知道什么，而不是人类演示者知道什么。
ChatGPT 对输入措辞的调整或多次尝试相同的提示很敏感。例如，给定一个问题的措辞，模型可以声称不知道答案，但只要稍作改写，就可以正确回答。
该模型通常过于冗长并过度使用某些短语，例如重申它是 OpenAI 训练的语言模型。这些问题源于训练数据的偏差（训练者更喜欢看起来更全面的更长答案）和众所周知的过度优化问题。1 2
理想情况下，当用户提供模棱两可的查询时，模型会提出澄清问题。相反，我们当前的模型通常会猜测用户的意图。
虽然我们已努力使模型拒绝不当请求，但它有时会响应有害指令或表现出有偏见的行为。我们正在使用Moderation API来警告或阻止某些类型的不安全内容，但我们预计它目前会有一些漏报和漏报。我们渴望收集用户反馈，以帮助我们正在进行的改进该系统的工作。
迭代部署
今天发布的 ChatGPT 研究版本是 OpenAI迭代部署越来越安全和有用的人工智能系统的最新一步。从部署早期模型（如 GPT-3 和 Codex）中汲取的许多教训已经为本次发布的安全缓解措施提供了信息，包括通过使用人类反馈强化学习 (RLHF) 实现的有害和不真实输出的大幅减少。