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Bayesian Preference Elicitation with Language Models: Optimizing User Preferences


Core Concepts
Aligning AI systems with user preferences using language models and Bayesian Optimal Experimental Design to optimize informative queries efficiently.
Abstract
The study introduces OPEN, a framework combining LMs and BOED for preference elicitation. It outperforms existing methods in user studies by optimizing query informativity while remaining adaptable to real-world domains. Key Points: OPEN combines LMs and BOED for preference elicitation. LM-only approaches struggle with feature weightings. OPEN outperforms LM-only methods in predicting human preferences. Users find LM open-ended questions repetitive. Feature weightings are crucial for accurate predictions. The study explores the importance of feature weightings in preference learning and highlights the limitations of LM-only approaches. It also discusses ethical considerations and future research directions.
Stats
In user studies, we find that OPEN outperforms existing LM- and BOED-based methods for preference elicitation. We use GPT-4 as the LM for all experiments. We prompt the LM with a general description of the domain to extract NL features.
Quotes
"OPEN can make predictions that better align with human preferences than a prompted LM." "LM’s open-ended questions were repetitive and overreliant on the LM’s prior over user preferences."

Key Insights Distilled From

by Kunal Handa,... at arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05534.pdf
Bayesian Preference Elicitation with Language Models

Deeper Inquiries

How can OPEN be adapted to other preference-learning domains beyond content recommendation?

OPEN can be adapted to other preference-learning domains by adjusting the feature set and the types of questions asked during the elicitation process. For different domains, such as personalized product recommendations or movie suggestions, the features extracted by the LM would need to reflect the relevant aspects of those domains. The Bayesian model used in OPEN could still guide the selection of informative queries based on these new features. Additionally, adapting OPEN for different domains may involve modifying the prediction step to align with specific preferences unique to that domain.

What are the potential risks associated with using preference data collected by AI systems?

There are several potential risks associated with using preference data collected by AI systems. One major risk is privacy concerns related to personal preferences being stored and potentially shared without consent. There is also a risk of bias in how preferences are interpreted and utilized, leading to reinforcement or amplification of existing biases present in society. Furthermore, there is a risk of manipulation if user preferences are exploited for targeted advertising or influencing behavior without users' awareness.

How can feature weightings be effectively communicated to users in an interactive setting?

To effectively communicate feature weightings to users in an interactive setting, it's essential to provide clear explanations and visualizations that help users understand how each feature contributes to their overall preferences. This could involve presenting relative importance rankings visually through graphs or charts, providing examples that illustrate how different features impact decision-making, and offering real-time feedback on how changing weights affect outcomes. Using natural language explanations alongside visual aids can enhance user comprehension and engagement with feature weightings presented during interactions within OPEN framework.
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