Bayesian Optimization for Preference Elicitation with Large Language Models

Helping users find their preferred items through natural language dialogues is a challenge. Traditional methods are inefficient, especially when users are unfamiliar with most items. Large language models (LLMs) like GPT-3 offer a potential solution due to their ability to understand and generate human-like text.

PEBOL Algorithm Overview

Modeling User Preferences: PEBOL assumes a hidden utility function to determine user preferences for each item based on its description.
Natural Language Queries: PEBOL uses decision-theoretic strategies to prompt the LLM to generate short, aspect-based queries about specific items.
Inferring Preferences via NLI: PEBOL uses a Natural Language Inference model to predict user preferences based on their responses.
Bayesian Belief Updates: PEBOL updates its probabilistic beliefs about user preferences based on predicted preferences.
Repeat: The process repeats to identify the user’s most preferred items.

The key innovation is using LLMs for natural query generation while leveraging Bayesian optimization to guide the conversational flow. This approach reduces the context needed for each LLM prompt and provides a principled way to balance the exploration-exploitation trade-off.

PEBOL Performance

In simulated preference elicitation dialogues, PEBOL achieved significant improvements in Mean Average Precision at 10 (MAP@10) over a monolithic GPT-3.5 baseline (MonoLLM) across various datasets. PEBOL also exhibited robustness against performance drops and outperformed MonoLLM under simulated user noise conditions.

AI Solutions for Business

Bayesian Optimization for Preference Elicitation with Large Language Models offers a promising new paradigm for building AI systems that can understand user preferences and provide personalized recommendations through natural language conversations.

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