# CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM

arXiv: https://arxiv.org/abs/2606.00846 · HTML: https://mlsanity.com/paper/2606.00846/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 30 May 2026.

## Builds on (most cited first, 22 of 22)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://mlsanity.com/paper/2305.05176/) (2023)
- [RouteLLM: Learning to Route LLMs with Preference Data](https://mlsanity.com/paper/2406.18665/) (2024)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing](https://mlsanity.com/paper/2404.14618/) (2024)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [HelpSteer2-Preference: Complementing Ratings with Preferences](https://mlsanity.com/paper/2410.01257/) (2024)
- [Contextual Dueling Bandits](https://mlsanity.com/paper/1502.06362/) (2015)
- [Adversarial Bandits with Knapsacks](https://mlsanity.com/paper/1811.11881/) (2018)
- [Latent Bandits Revisited](https://mlsanity.com/paper/2006.08714/) (2020)
- [Sparse Dueling Bandits](https://mlsanity.com/paper/1502.00133/) (2015)
- [Causal LLM Routing: End-to-End Regret Minimization from Observational Data](https://mlsanity.com/paper/2505.16037/) (2025)
- [Latent Contextual Bandits and their Application to Personalized Recommendations for New Users](https://mlsanity.com/paper/1604.06743/) (2016)
- [Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback](https://mlsanity.com/paper/2404.10776/) (2024)
- [Multi-Armed Bandits with Network Interference](https://mlsanity.com/paper/2405.18621/) (2024)
- [LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits](https://mlsanity.com/paper/2410.01735/) (2024)
- [Survey: Multi-Armed Bandits Meet Large Language Models](https://mlsanity.com/paper/2505.13355/) (2025)
- [LLM Routing with Dueling Feedback](https://mlsanity.com/paper/2510.00841/) (2025)
- [Early ChatGPT User Portrait through the Lens of Data](https://mlsanity.com/paper/2312.10078/) (2023)
- [Online Clustering of Dueling Bandits](https://mlsanity.com/paper/2502.02079/) (2025)

## Abstract (arXiv)

Users increasingly face the challenge of selecting an appropriate LLM for a given task from a rapidly growing pool of LLMs, each with distinct but often opaque latent properties. Compounding this challenge, users may lack the vocabulary or awareness to explicitly articulate the characteristics they value in an LLM's responses or deployment. We propose an interaction-efficient active learning framework in which a dueling bandit algorithm iteratively selects pairs of LLMs, collects user feedback about their responses, and updates its belief about the user's latent preferences. We introduce a novel belief-aware upper confidence bound strategy that balances exploration of the model pool with exploitation of inferred preferences, enabling efficient alignment between user needs and LLM capabilities under user-specified cost and time budgets. Through diverse experiments on LLMs and human studies, we experimentally verify that our model can efficiently match well-aligned LLMs to users at a lower cost.