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#An example of a rough draft on bulling mod#

We know that many limitations remain as discussed above and we plan to make regular model updates to improve in such areas. We’re eager to collect user feedback to aid our ongoing work to improve this system. We’re using the Moderation API to warn or block certain types of unsafe content, but we expect it to have some false negatives and positives for now. While we’ve made efforts to make the model refuse inappropriate requests, it will sometimes respond to harmful instructions or exhibit biased behavior.Instead, our current models usually guess what the user intended. Ideally, the model would ask clarifying questions when the user provided an ambiguous query.These issues arise from biases in the training data (trainers prefer longer answers that look more comprehensive) and well-known over-optimization issues.

The model is often excessively verbose and overuses certain phrases, such as restating that it’s a language model trained by OpenAI.For example, given one phrasing of a question, the model can claim to not know the answer, but given a slight rephrase, can answer correctly. ChatGPT is sensitive to tweaks to the input phrasing or attempting the same prompt multiple times.Fixing this issue is challenging, as: (1) during RL training, there’s currently no source of truth (2) training the model to be more cautious causes it to decline questions that it can answer correctly and (3) supervised training misleads the model because the ideal answer depends on what the model knows, rather than what the human demonstrator knows.

ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers.

We performed several iterations of this process. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. To collect this data, we took conversations that AI trainers had with the chatbot. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. We gave the trainers access to model-written suggestions to help them compose their responses. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides-the user and an AI assistant. We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. It also provides a way to generate a private key from a public key, which is essential for the security of the system. In this way, Fermat’s Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography.

#An example of a rough draft on bulling mod#

To decrypt the message, the recipient simply computes m = c b m o d n m = c^b \bmod n m = c b mod n, which (by Fermat’s Little Theorem) is equivalent to m = ( m a ) b m o d n = m ( a b ) m o d n = m 1 m o d n = m m o d n m = (m^a)^b \bmod n = m^(ab) \bmod n = m^1 \bmod n = m \bmod n m = ( m a ) b mod n = m ( ab ) mod n = m 1 mod n = m mod n. To encrypt a message with the user’s public key ( n, a ) (n, a) ( n, a ), we first convert the message into a number m m m (using some agreed-upon scheme), and then compute the encrypted message c c c as c = m a m o d n c = m^a \bmod n c = m a mod n. This means that when we multiply a a a and b b b together, the result is congruent to 1 1 1 modulo n n n. The user’s private key would be the pair ( n, b ) (n, b) ( n, b ), where b b b is the modular multiplicative inverse of a modulo n n n. The user’s public key would then be the pair ( n, a ) (n, a) ( n, a ), where aa is any integer not divisible by p p p or q q q. We might choose two large prime numbers, p p p and q q q, and then compute the product n = p q n = pq n = pq. For example, suppose we want to generate a public-key cryptography system for a user with the initials “ABC”. One way to generate these keys is to use prime numbers and Fermat’s Little Theorem. 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 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. Fermat’s Little Theorem is used in cryptography in several ways.