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CHATGPT: OPTIMIZING LANGUAGE MODELS FOR DIALOGUE

ChatGPT is a natural language processing tool driven by AI technology that
allows you to have human-like conversations and much more with a chatbot. The
language model can answer questions, and assist you with tasks such as composing
emails, essays, and code.

Try ChatGPT Read about ChatGPT Plus


ChatGPT is a sibling model to InstructGPT, which is trained to follow an
instruction in a prompt and provide a detailed response.

We are excited to introduce ChatGPT to get users’ feedback and learn about its
strengths and weaknesses. During the research preview, usage of ChatGPT is free.
Try it now at chat.openai.com.


SAMPLES

In the following sample, ChatGPT asks the clarifying questions to debug code.

November 30, 2022

Author: OpenAI


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METHODS

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. 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 gave the trainers access to model-written
suggestions to help them compose their responses. We mixed this new dialogue
dataset with the InstructGPT dataset, which we transformed into a dialogue
format.

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. To collect this data, we took conversations that AI trainers had with
the chatbot. We randomly selected a model-written message, sampled several
alternative completions, and had AI trainers rank them. Using these reward
models, we can fine-tune the model using Proximal Policy Optimization. We
performed several iterations of this process.



ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished
training in early 2022. You can learn more about the 3.5 series here. ChatGPT
and GPT-3.5 were trained on an Azure AI supercomputing infrastructure.


LIMITATIONS

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

2. ChatGPT is sensitive to tweaks to the input phrasing or attempting the same
prompt multiple times. 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.

3. The model is often excessively verbose and overuses certain phrases, such as
restating that it’s a language model trained by OpenAI. These issues arise from
biases in the training data (trainers prefer longer answers that look more
comprehensive) and well-known over-optimization issues.

4. Ideally, the model would ask clarifying questions when the user provided an
ambiguous query. Instead, our current models usually guess what the user
intended.

5. While we’ve made efforts to make the model refuse inappropriate requests, it
will sometimes respond to harmful instructions or exhibit biased behavior. 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. We’re eager to
collect user feedback to aid our ongoing work to improve this system.


RELATED RESEARCH

View all research


FORECASTING POTENTIAL MISUSES OF LANGUAGE MODELS FOR DISINFORMATION CAMPAIGNS
AND HOW TO REDUCE RISK

Jan 11, 2023


POINT-E: A SYSTEM FOR GENERATING 3D POINT CLOUDS FROM COMPLEX PROMPTS

Dec 16, 2022


SCALING LAWS FOR REWARD MODEL OVEROPTIMIZATION

Oct 19, 2022


INTRODUCING WHISPER

Sep 21, 2022

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