Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF.

In this article, we explore the challenges of using reinforcement learning with hidden context in AI systems. Hidden context refers to the factors that can influence how users provide feedback to an AI assistant, such as their biases or preferences. The authors use the example of a college admissions chatbot to illustrate how naive preference learning can lead to undesirable consequences when the population providing feedback is biased towards high-income students.
The article begins by explaining that most reinforcement learning (RL) algorithms rely on feedback from users to train AI models. However, in many scenarios, the feedback provided may not accurately reflect the user’s true preferences due to hidden context. To address this issue, the authors propose a method called "Active Learning with Hidden Context" (ALHC).
The authors then demonstrate how ALHC can help mitigate the consequences of naive preference learning in the college admissions chatbot example. They show that by actively seeking feedback from users that are more representative of the target population, the chatbot can learn to provide more informative and relevant responses.
Throughout the article, the authors use analogies and metaphors to help demystify complex concepts. For instance, they compare the process of learning with hidden context to a game of "Jenga," where players must carefully consider which blocks to remove in order to build a stable tower. Similarly, they describe the act of providing feedback to an AI assistant as "giving a restaurant review," emphasizing that users must consider not only their immediate preferences but also the broader context in which the AI is operating.
The authors conclude by highlighting the importance of considering hidden context in AI development and the potential benefits of using ALHC to improve outcomes. They note that while naive preference learning can lead to undesirable consequences, active learning with hidden context can help ensure that AI systems are trained on more representative feedback and provide better overall performance.
In summary, this article explores the challenges of training AI models with reinforcement learning when there is hidden context in user feedback. The authors propose a method called Active Learning with Hidden Context to mitigate these issues and improve outcomes. Through engaging analogies and metaphors, they demystify complex concepts and highlight the importance of considering hidden context in AI development.