Introduction ‌

ChatGPT, the revolutionary language model developed by OpenAI, has attracted widespread interest from various industries ⁠ thanks to its capacity to produce answers resembling those made by actual individuals. Although, has it ever crossed your mind how ⁠ ChatGPT manages to achieve such remarkable effectiveness? Within this all-inclusive manual, we shall delve into ⁠ the functioning of ChatGPT’s internal mechanism. To be specific, we will concentrate on ⁠ the RLHF algorithm that empowers it. ​

Understanding Algorithmic Bias ‍

An obstacle encountered by large AI models is algorithmic bias, In these ⁠ cases, the model displays consistent errors or outcomes that lack justification. This bias can arise due to the biased nature ⁠ of the data used for training the model. As an illustration, certain occupational labels could be more prone to being ⁠ linked with particular genders, thus sustaining damaging stereotypes across various fields. ​

Challenges in Training Language Models

To train language models for various use cases, including tasks such as storytelling with creativity, providing accurate ⁠ information, or coding automation requires a loss function that effectively captures all the required attributes. While utilizing metrics like BLEU or ROUGE to enhance output quality, they ⁠ possess constraints when it comes to accurately representing human preferences.

Reinforcement Learning with Human ⁠ Feedback (RLHF) ​

In order to address the constraints of loss functions ⁠ and metrics, RLHF was introduced by OpenAI. The RLHF algorithm involves incorporating human feedback as a performance ⁠ measure or loss function during the training process. This grants the language model to learn ⁠ from human-like behavior and preferences. ⁠

The RLHF Training Process ​

The representation of the RLHF training process ⁠ can be shown in a flowchart. In this process, an “Agent” (RL algorithm) ⁠ observes the environment and takes actions. The environment provides rewards to the ⁠ Agent based on its actions. Regarding RLHF, the calculation of rewards involves human feedback ⁠ rather than relying solely on the environment. ​

Acquiring the skill of summarizing through feedback ⁠ from humans from Human Feedback

To enhance our comprehension of RLHF within NLP, we examine the paper “Learning to Summarize from Human Feedback.” ⁠ A language model guided by human feedback was suggested in this research paper for summarizing tasks. To train the model, comparison data was utilized, where humans ⁠ ranked generated summaries to create a reward model.

RLHF in ChatGPT ​

The training process for ChatGPT ⁠ involves three key steps: ​

Pretraining Task: Supervised learning is utilized to pretrain the language model, Training data is ⁠ generated by AI trainers who assume both user and AI assistant roles. ​

Preparing the Reward Model: The ranking of generated text’s quality relies on human ⁠ feedback., and a reward model is constructed based on this feedback. ⁠

RL-based Fine-tuning LLM: Fine-tuning of the language model involves ⁠ utilizing the Proximal Policy Optimization (PPO) algorithm. through integrating rewards obtained from a reward model ⁠ and imposing restrictions on how it operates.

Conclusion ​

By involving human feedback in the training process, Through learning from humans, ChatGPT can ⁠ exhibit behaviors that resemble human behavior and achieve high performance in diverse applications. OpenAI’s strategy has been fruitful, However, it also prompts inquiries ⁠ regarding how AI affects human creativity and learning. To set apart AI-generated text from human-written content, one proposal is to ⁠ employ watermarking techniques, drawing inspiration from deepfakes used in computer vision. As AI technology evolves, OpenAI will persist in enhancing its RLHF ⁠ algorithm to increase the effectiveness and dependability of ChatGPT.