Improving Language Agent Alignment with User Preferences through Interactive Learning from Edits

Core Concepts

Learning descriptive user preference from implicit feedback in the form of user edits can improve the alignment of language agents with user needs, while reducing the cost of user edits over time.

Abstract

The article presents a framework called PRELUDE (PREference Learning from User's Direct Edits) for interactive learning of language agents based on user edits to the agent's output. In a typical setting such as writing assistants, users interact with a language agent to generate a response, and may edit the agent's response to personalize it based on their latent preference.
The key insights are:

User edits provide natural feedback that can be leveraged to improve the agent's alignment with the user's preference, without the need for expensive explicit preference collection.

User preference can be complex, subtle, and context-dependent, making it challenging to learn. The article proposes a simple yet effective algorithm called CIPHER (Consolidates Induced Preferences based on Historical Edits with Retrieval) to address this.

CIPHER infers a textual description of the user's latent preference for a given context by leveraging a large language model (LLM) and retrieving similar past contexts. This learned preference is then used to generate future responses, leading to lower user edit costs over time.

Compared to baselines that directly use past user edits or learn context-agnostic preferences, CIPHER achieves the lowest cumulative user edit cost on two interactive writing assistant tasks - summarization and email writing. It also has lower computational expense than baselines that do not learn preferences.

The authors' analysis shows that the preferences learned by CIPHER have significant similarity to the ground truth latent preferences, demonstrating its effectiveness in capturing user needs.

Stats

The cumulative user edit cost (edit distance) for the summarization task is 33,926 for CIPHER-1-MPNET, compared to 48,269 for no learning and 65,218 for explore-then-exploit LPI.
The cumulative user edit cost for the email writing task is not reported for CIPHER, but the best baseline (ICL-edit-5-BERT) has a cost of 30,949.
The preference classification accuracy of CIPHER-1-MPNET is 0.520 on the summarization task, compared to 0.218 for explore-then-exploit LPI and 0.233 for continual LPI.
The total token expense (input + output tokens) for CIPHER-1-MPNET on the summarization task is 2.74 x 10^5, compared to 1.99 x 10^5 for explore-then-exploit LPI and 8.89 x 10^5 for continual LPI.

Quotes

None.

Key Insights Distilled From

Aligning LLM Agents by Learning Latent Preference from User Edits

by Ge Gao,Alexe... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15269.pdf

Deeper Inquiries

How can the PRELUDE framework be extended to handle more complex user preferences that evolve over time or depend on information not available in the context?

The PRELUDE framework can be extended to handle more complex user preferences by incorporating mechanisms for capturing the evolution of preferences over time and incorporating external information that may not be directly available in the context. One approach to address evolving preferences is to introduce a dynamic preference model that can adapt to changes in user preferences over time. This model can incorporate historical data on user edits and preferences to track changes and update the inferred preferences accordingly. Additionally, techniques from reinforcement learning, such as incorporating a reward signal based on user satisfaction with the responses, can help the agent adapt to evolving preferences.
To handle preferences that depend on information not available in the context, the framework can leverage external data sources or user profiles to enrich the context. By integrating user-specific information or contextual data from external sources, the agent can better understand and adapt to user preferences that are influenced by factors beyond the immediate context. This can involve using user profiles, historical interactions, or external databases to infer and incorporate relevant preferences into the response generation process.

How can the preference learning process be made more transparent and interpretable to the user, beyond just showing the learned preference text?

To enhance transparency and interpretability of the preference learning process for the user, the PRELUDE framework can incorporate visualization tools or interactive interfaces that provide insights into how preferences are inferred and utilized in generating responses. One approach is to provide a preference dashboard that displays the learned preferences in a user-friendly format, such as visual summaries or graphs that highlight key aspects of the preferences. This can help users understand how their edits influence the agent's responses and provide them with a clear overview of their preferences.
Another strategy is to enable user feedback mechanisms that allow users to provide direct input on the inferred preferences and adjust them as needed. By incorporating feedback loops where users can validate or modify the learned preferences, the framework can empower users to actively participate in shaping the agent's behavior based on their preferences. Additionally, providing explanations or justifications for why certain preferences were inferred can enhance the transparency of the learning process and build trust with the user.

What other applications beyond writing assistants could benefit from the PRELUDE framework for learning from user edits?

The PRELUDE framework for learning from user edits can be applied to a wide range of interactive systems beyond writing assistants to improve user-agent alignment and enhance user experience. Some potential applications include:

Customer Service Chatbots: By learning from user edits in customer interactions, chatbots can adapt to individual preferences and provide more personalized and effective responses, leading to improved customer satisfaction.

Personalized Recommender Systems: Incorporating user edits to refine recommendations can enhance the relevance and accuracy of suggestions in areas such as e-commerce, content streaming, and online platforms.

Virtual Assistants: Learning from user edits can help virtual assistants tailor responses to user preferences in tasks like scheduling, reminders, and information retrieval, improving the overall user interaction.

Educational Platforms: By analyzing user edits in learning materials, educational systems can adapt content delivery to individual learning styles and preferences, creating a more engaging and personalized learning experience.

Overall, the PRELUDE framework has the potential to enhance user-agent interactions in various domains by leveraging user edits to learn and adapt to user preferences effectively.