Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
(2405.00981)Abstract
Designing preference elicitation (PE) methodologies that can quickly ascertain a user's top item preferences in a cold-start setting is a key challenge for building effective and personalized conversational recommendation (ConvRec) systems. While LLMs constitute a novel technology that enables fully natural language (NL) PE dialogues, we hypothesize that monolithic LLM NL-PE approaches lack the multi-turn, decision-theoretic reasoning required to effectively balance the NL exploration and exploitation of user preferences towards an arbitrary item set. In contrast, traditional Bayesian optimization PE methods define theoretically optimal PE strategies, but fail to use NL item descriptions or generate NL queries, unrealistically assuming users can express preferences with direct item ratings and comparisons. To overcome the limitations of both approaches, we formulate NL-PE in a Bayesian Optimization (BO) framework that seeks to generate NL queries which actively elicit natural language feedback to reduce uncertainty over item utilities to identify the best recommendation. We demonstrate our framework in a novel NL-PE algorithm, PEBOL, which uses Natural Language Inference (NLI) between user preference utterances and NL item descriptions to maintain preference beliefs and BO strategies such as Thompson Sampling (TS) and Upper Confidence Bound (UCB) to guide LLM query generation. We numerically evaluate our methods in controlled experiments, finding that PEBOL achieves up to 131% improvement in MAP@10 after 10 turns of cold start NL-PE dialogue compared to monolithic GPT-3.5, despite relying on a much smaller 400M parameter NLI model for preference inference.
Overview
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The paper combines Bayesian Optimization (BO) with LLMs to improve Preference Elicitation (PE) in conversational recommendation systems, particularly effective in 'cold-start' situations where initial user preferences are unknown.
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The new framework, PEBOL, utilizes BO to maintain a probabilistic model of user preferences updated through user feedback, and LLMs to generate and understand natural language, fostering more natural user interactions.
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Experimental results show that PEBOL outperforms existing models like GPT-3.5 in cold-start scenarios and demonstrates resilience to noisy user data, suggesting robust applications for personalized conversational systems in various sectors.
Understanding Bayesian Optimization and LLMs in Natural Language Preference Elicitation
Introduction to the Technique
The paper under discussion merges Bayesian Optimization (BO) with LLMs to enhance Preference Elicitation (PE) in systems that engage users through natural language. This innovative approach targets conversational recommendation systems that operate in "cold-start" settings—a scenario where initial user preferences aren't known.
BO traditionally excels at discovering user preferences by balancing exploration (learning new information) and exploitation (leveraging known information). However, it struggles with understanding and generating natural language dialogues. LLMs, conversely, are adept at handling language but lack the strategic decision-making BO provides. The proposed solution, named PEBOL (Preference Elicitation with Bayesian Optimization augmented LLMs), integrates these strengths, setting a new stage for conversational recommendation systems.
Key Components of the Approach
Bayesian Optimization Framework
PEBOL applies BO to PE by maintaining a probabilistic model of user preferences in the form of Bayesian "beliefs". These beliefs about item utilities are updated based on user’s feedback, which allows the system to carefully select what to inquire next about based on current knowledge and uncertainties.
Natural Language Handling via LLMs
LLMs in PEBOL are used to generate and understand natural language queries and responses. Queries generated by LLMs prompt users to disclose their preferences in a conversational format, making the interaction more user-friendly and less rigid compared to traditional PE approaches.
Observations from Experiments
The system was tested against GPT-3.5 in various settings, such as different datasets and levels of user-response noise. Here are some notable findings:
- Effectiveness in Cold-Start Scenarios: PEBOL displayed significant improvements in performance measures like MAP@10, achieving up to 131% better outcomes than GPT-3.5 alone in early interactions (10 dialogue turns).
- Robustness to Noise: PEBOL maintained superior performance even when users' responses included noise, illustrating its effectiveness in less-than-ideal real-world scenarios.
- Comparisons of Different Strategies: Various acquisition strategies were explored within PEBOL, such as Thompson Sampling and Upper Confidence Bound, which helped in understanding how different methods affect the exploration-exploitation balance.
Implications and Future Directions
Practical Applications
For businesses, this research paves the way for more effective conversational recommendation systems that can start functioning effectively without needing extensive initial data about user preferences. This can be particularly valuable in e-commerce, content recommendation, and any service that benefits from personalized user engagement.
Theoretical Contributions
Theoretically, this work broadens the understanding of integrating decision-theoretical reasoning with language models, a crossover not often explored in machine learning. It sets groundwork for future studies on how different AI domains can be blended for enhanced user interaction.
Speculations on Future Developments
Looking ahead, the successful integration of BO and LLMs suggests potential extensions where more complex models of user behavior could be incorporated, potentially taking into account varying user moods, contexts, or even indirect preference signals within longer dialogues.
Conclusion
The intersection of Bayesian Optimization and LLMs through the PEBOL framework represents a significant step forward in making conversational systems more responsive and effective right from the start. This fusion not only amplifies the strengths of both methods but also opens new horizons in personalized AI interactions, enhancing both user experience and system performance.
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