让大模型生成更丰富，智能挑选不同风格的响应模型。
DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models
- 用上下文感知的多样性评分，动态选择最合适的生成模型。
- 在保持生成质量的同时，使输出多样性提升23.6%（相比纯质量方法）。
- 适合需要多风格输出的应用，如创意设计、内容生成场景。
生成式AI服务的扩展凸显了根据用户提示动态选择合适模型的需求。现有方法仅基于最大化提示相关保真度评分（如文本到图像生成中的CLIP-Score）进行模型选择，但忽视了生成结果的多样性，可能导致输出单一。本文提出一种多样性感知的核化上置信界（DAK-UCB）方法，作为上下文老虎机算法，实现生成模型的在线选择。该方法融合保真度与多样性指标，基于提示感知的多样性评分函数，通过前轮生成中提示-输出对的两样本期望进行建模。具体采用联合核距离与核熵度量。实验表明，DAK-UCB在维持生成保真度的同时显著提升多样性，在多个提示序列下表现优于基准方法。代码已公开于https://github.com/Donya-Jafari/DAK-UCB。
原文摘要 · Abstract (English)
The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms to select an appropriate available model to respond to a user's prompts. Recent works have proposed offline and online learning formulations to identify the optimal generative AI model for an input prompt, based solely on maximizing prompt-based fidelity evaluation scores, e.g., CLIP-Score in text-to-image generation. However, such fidelity-based selection methods overlook the diversity of generated outputs, and hence, they can fail to address potential diversity shortcomings in the generated responses. In this paper, we introduce the Diversity-Aware Kernelized Upper Confidence Bound (DAK-UCB) method as a contextual bandit algorithm for the online selection of generative models with diversity considerations. The proposed DAK-UCB method incorporates both fidelity and diversity-related metrics into the selection process. We design this framework based on prompt-aware diversity score functions that decompose to a two-sample-based expectation over prompt-output pairs in the previous generation rounds. Specifically, we illustrate the application of our framework using joint kernel distance and kernel entropy measures. Our experimental results demonstrate the effectiveness of DAK-UCB in promoting diversity-aware model selection while maintaining fidelity in the generations for a sequence of prompts. The code is available at https://github.com/Donya-Jafari/DAK-UCB.
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