Reinforcement learning with human feedback (RLHF) is a cutting-edge technique that has been gaining popularity in recent years as a means of improving the performance of large language models. It’s a powerful way to train these models using human feedback, and the human input component has many similarities to search evaluation. Both approaches are designed to improve the quality and relevance of the output through the use of subjective human input. While for search evaluation, humans focus on ranking search results, as part of RLHF, however, humans focus on generating natural language prompts, responses that are representative of an input prompt, and preference rankings of those responses.
At its core, RLHF is a technique that combines reinforcement learning with human feedback, where human preferences are used as a reward signal to guide the model towards generating high-quality language outputs. By using a diverse set of feedback providers, RLHF can help the model learn to generate text that is more representative of different viewpoints, making it more versatile and useful in a variety of contexts.
One of the key benefits of RLHF for business leaders is that it can help improve the performance of large language models by making them more adaptable to user needs. This is particularly important in industries where customer satisfaction is critical, such as healthcare, finance, and e-commerce. With RLHF, companies can use human feedback to train their models to better understand and respond to user needs, ultimately leading to higher customer satisfaction and engagement.
At Appen, we have deep expertise in delivering large-scale data for search relevance and are now applying our search expertise to support the growth of generative AI models through RLHF. We have worked with many clients on improving the performance of large language models, and we see a close alignment between RLHF and our mission to help companies create high-quality, relevant content that engages users.
So, how does RLHF actually work? The process typically involves three main steps:
- collect a dataset of human-generated prompts and responses and fine-tune a language model.
- collect human-generated rankings of model responses to prompts and train a reward model.
- perform reinforcement learning.
In the prompt-response pair generation step, a dataset of human-written prompts and appropriate human-written responses is assembled. This could be anything from a product description to a customer query. Some of the subject matter may be accessible to a wide audience, while other topics may require domain knowledge. This dataset is then used to fine-tune the language model using supervised learning.
In the response ranking step, multiple responses to the same prompt are sampled from the model, for each of a large set of prompts. These responses are then presented to human feedback providers, who rank them according to their preference. The ranking data is then used to train a reward model. The reward model predicts which output humans would prefer.
PERFORM REINFORCEMENT LEARNING
Finally, the reward model is used as a reward function, and the language model is fined-tuned to maximize this reward. In this way, the language model is taught to “prefer” the types of responses also preferred by the group of human evaluators.
One of the key advantages of RLHF is that it allows models to learn from a diverse set of feedback providers, which can help them generate responses that are more representative of different viewpoints and user needs. This can help improve the quality and relevance of the output, making the model more useful in a variety of contexts.
Another benefit of RLHF is that it can help reduce bias in generative AI models. Traditional machine learning approaches can be prone to bias, as they rely heavily on training data that may be skewed towards certain demographics or viewpoints. By using human feedback, RLHF can help models learn to generate more balanced and representative responses, reducing the risk of bias.
We’ve witnessed firsthand the power of RLHF in improving the performance of large language models. By using human feedback to train models, we have helped our clients create more engaging and relevant content that meets the needs of their users. We believe that RLHF will continue to be a critical tool for businesses looking to leverage generative AI to improve customer satisfaction and engagement.
RLHF is a cutting-edge technique that combines reinforcement learning with human feedback to improve the performance of large language models. By using a diverse set of feedback providers, RLHF can help models learn to generate more representative and relevant responses, making them more adaptable to user needs. RLHF can also help reduce bias in generative AI models and accelerate the learning process, leading to more efficient and cost-effective training.
As the field of generative AI continues to evolve, we believe that RLHF will play an increasingly important role in helping businesses create high-quality, engaging content that meets the needs of their users.