Summary of Cost-effective Online Multi-llm Selection with Versatile Reward Models, by Xiangxiang Dai et al.
Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
by Xiangxiang Dai, Jin Li, Xutong Liu, Anqi Yu, John C.S. Lui
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 introduces C2MAB-V, a novel online model that optimizes the selection of multiple large language models (LLMs) for various collaborative tasks. This approach addresses the challenge of diverse LLM pricing structures and task-specific reward models. C2MAB-V differs from traditional methods by incorporating cost consideration and utilizing a combinatorial search space. The model is designed to manage the exploration-exploitation trade-off across different LLMs, balancing cost and reward for diverse tasks. To achieve this, it decomposes an NP-hard integer linear programming problem into a relaxed form, utilizes a discretization rounding scheme, and performs continual online updates based on feedback. Theoretically, C2MAB-V offers strict guarantees over versatile reward models, matching state-of-the-art results in some cases. Empirically, the model demonstrates effective performance and cost-efficiency with nine LLMs for three application scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to pick the best combination of language models for different tasks. Language models are like super smart computers that can understand and generate human-like text. They’re really good at helping us do things like answer questions, translate languages, or even write new texts. But they come in different forms and cost different amounts of money. The researchers created a special system called C2MAB-V that helps choose the best combination of language models for each task. This system takes into account how much each model costs and what kind of rewards it can give us. It’s like having a super smart personal assistant that helps you get the most out of your language models! |
