Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
<|reference_start|>Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation: 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 large language models (LLMs) enable 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 exploration and exploitation of user preferences towards an arbitrary item set. In contrast, traditional Bayesian optimization PE methods define theoretically optimal PE strategies, but cannot generate arbitrary NL queries or reason over content in NL item descriptions -- requiring users to express preferences via ratings or comparisons of unfamiliar items. To overcome the limitations of both approaches, we formulate NL-PE in a Bayesian Optimization (BO) framework that seeks to actively elicit NL feedback to identify the best recommendation. Key challenges in generalizing BO to deal with natural language feedback include determining: (a) how to leverage LLMs to model the likelihood of NL preference feedback as a function of item utilities, and (b) how to design an acquisition function for NL BO that can elicit preferences in the infinite space of language. We demonstrate our framework in a novel NL-PE algorithm, PEBOL, which uses: 1) Natural Language Inference (NLI) between user preference utterances and NL item descriptions to maintain Bayesian preference beliefs, and 2) BO strategies such as Thompson Sampling (TS) and Upper Confidence Bound (UCB) to steer LLM query generation. We numerically evaluate our methods in controlled simulations, finding that after 10 turns of dialogue, PEBOL can achieve an MRR@10 of up to 0.27 compared to the best monolithic LLM baseline's MRR@10 of 0.17, despite relying on earlier and smaller LLMs.<|reference_end|>
@article{austin2024bayesian,
    title={Bayesian Optimization with LLM-Based Acquisition Functions for Natural
  Language Preference Elicitation},
    author={David Eric Austin, Anton Korikov, Armin Toroghi, Scott Sanner},
    journal={arXiv preprint arXiv:2405.00981},
    year={2024},
    doi={10.1145/3640457.3688142},
    archivePrefix={arXiv},
    eprint={2405.00981},
    primaryClass={cs.AI cs.CL}
}