Reinforcement Learning from Human Feedback

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Reinforcement learning from human feedback (RLHF) is used in Machine Learning (ML) to improve the accuracy and language of agent or model responses using human feedback. The feedback can be captured passively, based on edits to outputs, or more actively through numeric scoring of responses or natural language assessments.


Why is Reinforcement Learning From Human Feedback Important?

RLHF is very useful when feedback is sparse or “noisy.” When the ML function provides natural language or text summarization, humans can easily judge the quality, which is challenging to do accurately using an algorithmic approach. The RLHF model can fine-tune its performance using positive and negative feedback by having humans rank outputs from good to bad.


Learning Methods

Humans can provide explicit feedback to a learning algorithm by editing output, which can be reviewed by the algorithm as guidance. Tuning usually begins with the use of training datasets. These include the prompt dataset containing unlabeled prompts and a human preference dataset that contains pairs of candidate responses, including labels indicating the preferred prompt response. A more hands-off approach is used during the reinforcement phase by biasing the learning toward the conversations that provide the best ratings of the agent’s output. Human trainers can provide feedback about what was done well and less well for more sophisticated or nuanced subjects.


Applications of RLHF

There are many current and emerging applications for RLHF. Here are some examples:

Conversational Chatbots

Conversational chatbots usually start with a partially pre-trained model, and then human trainers tune the base model. When deployed into production, the chatbots solicit user input to score their understanding and responses. The higher-scoring conversations are used to set positive reinforcement benchmarks for continuous improvement.

GPT Dialogs

Chats involving a GPT-driven conversation can use positive feedback from humans to guide their learning. Pre-trained plug-ins that include knowledge of various domains can be developed.

Text Summarization and Translation

Human reviewers read summaries and either make or suggest edits that the machine learning model uses as input for successive attempts. The same approach works well for translation and transcription services where the model has to adapt to subtle local differences.


Challenges With RLHF

Artificial intelligence (AI)-driven conversations still have a way to go to be as natural as real human conversations, but they are maturing fast. The reliance on human subjectivity can be problematic because different people’s views vary. Conversations rarely use poor grammar but can have flaws based on the trainer’s use of language. For example, if the trainer is biased or overuses colloquialisms, the algorithm will pick up those traits. A different trainer must flag these traits negatively to train them out of use. Imagine training your chatbot using too many press releases and marketing content. The result will be that overusing hyperbole impacts the chat agent’s credibility. A model that has been undertrained often resorts to repetition, which can tire or irritate the consumer.


Benefits of RLHF

Below are many of the benefits of adopting RLHF:

  • Provides a way to continuously improve the accuracy and performance of chat-based conversations.
  • Enables the finer tuning of domain-specific dialogs using human input.
  • Allows chat agents to mimic language more naturally, improving customer service.
  • Provides an end user to deliver feedback that improves future interactions.
  • It enables humans to train AI to be better aligned with their interaction style, including having a more informal and less robotic persona.


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