Summary of Bayesian Optimization with Llm-based Acquisition Functions For Natural Language Preference Elicitation, by David Eric Austin et al.
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation
by David Eric Austin, Anton Korikov, Armin Toroghi, Scott Sanner
First submitted to arxiv on: 2 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to building effective and personalized conversational recommendation systems by designing preference elicitation methodologies that can quickly ascertain a user’s top item preferences in a cold-start setting. The authors hypothesize that monolithic large language models (LLMs) lack the multi-turn, decision-theoretic reasoning required to effectively balance exploration and exploitation of user preferences towards an arbitrary item set. To overcome this limitation, they formulate NL-PE in a Bayesian Optimization (BO) framework that seeks to actively elicit NL feedback to identify the best recommendation. The proposed algorithm, PEBOL, uses Natural Language Inference (NLI) between user preference utterances and NL item descriptions to maintain Bayesian preference beliefs, and BO strategies such as Thompson Sampling (TS) and Upper Confidence Bound (UCB) to steer LLM query generation. Experimental results show that PEBOL can achieve an MRR@10 of up to 0.27 compared to the best monolithic LLM baseline’s MRR@10 of 0.17. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help people find what they like by asking them questions in natural language. The idea is that if we can ask good questions and understand what people mean when they say something, we can make better recommendations for things they might like. To do this, the authors combine two existing ideas: large language models (LLMs) that can understand natural language, and Bayesian optimization (BO), which helps us find the best thing to try next. They call their new method PEBOL, and they test it in some simulations to see how well it works. |
Keywords
* Artificial intelligence * Inference * Optimization
