Academic

Scalable Prompt Routing via Fine-Grained Latent Task Discovery

arXiv:2603.19415v1 Announce Type: new Abstract: Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow performance gaps, existing approaches face significant challenges: manually defined task taxonomies cannot capture fine-grained capability distinctions, while monolithic routers struggle to differentiate subtle differences across diverse tasks. We propose a two-stage routing architecture that addresses these limitations through automated fine-grained task discovery and task-aware quality estimation. Our first stage employs graph-based clustering to discover latent task types and trains a classifier to assign prompts to discovered tasks. The second stage uses a mixture-of-experts architecture with task-specific prediction heads for specialized quality estimates. At inference, we aggregate predictions from bot

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Yunyi Zhang, Soji Adeshina, Patrick Guan, Ashwin Ganesh, Zhen Han, Vassilis N. Ioannidis, Huzefa Rangwala, George Karypis · · 1 min read · 9 views
#cs.CL #cs.AI #cs.LG

arXiv:2603.19415v1 Announce Type: new Abstract: Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow performance gaps, existing approaches face significant challenges: manually defined task taxonomies cannot capture fine-grained capability distinctions, while monolithic routers struggle to differentiate subtle differences across diverse tasks. We propose a two-stage routing architecture that addresses these limitations through automated fine-grained task discovery and task-aware quality estimation. Our first stage employs graph-based clustering to discover latent task types and trains a classifier to assign prompts to discovered tasks. The second stage uses a mixture-of-experts architecture with task-specific prediction heads for specialized quality estimates. At inference, we aggregate predictions from both stages to balance task-level stability with prompt-specific adaptability. Evaluated on 10 benchmarks with 11 frontier models, our method consistently outperforms existing baselines and surpasses the strongest individual model while incurring less than half its cost.

Executive Summary

This article proposes a novel approach to prompt routing in large language models, addressing the challenges of fine-grained capability distinctions and subtle differences across diverse tasks. The two-stage routing architecture employs graph-based clustering for automated task discovery and a mixture-of-experts architecture for task-aware quality estimation. Evaluated on 10 benchmarks with 11 frontier models, the method outperforms existing baselines and surpasses the strongest individual model while incurring less than half its cost. This achievement has significant implications for real-world applications, where model performance and cost-effectiveness are crucial. The proposed approach has the potential to revolutionize the field of large language models by enabling more efficient and effective task routing, ultimately leading to better user experiences and improved outcomes.

Key Points

  • ▸ Proposes a two-stage routing architecture for fine-grained task discovery and quality estimation
  • ▸ Employs graph-based clustering for automated task discovery
  • ▸ Uses a mixture-of-experts architecture for task-aware quality estimation
  • ▸ Outperforms existing baselines and surpasses the strongest individual model
  • ▸ Reduces costs by less than half compared to the strongest individual model

Merits

Strength in Task Discovery

The proposed approach employs graph-based clustering for automated task discovery, enabling the identification of fine-grained task types and subtleties that traditional methods often miss.

Task-Aware Quality Estimation

The mixture-of-experts architecture used in the second stage provides specialized quality estimates for each task, leading to more accurate and effective routing decisions.

Cost-Effectiveness

The proposed approach reduces costs by less than half compared to the strongest individual model, making it an attractive solution for real-world applications.

Demerits

Limited Generalizability

The proposed approach is evaluated on a specific set of benchmarks and models, and its generalizability to other domains and tasks remains unclear.

Computational Complexity

The graph-based clustering and mixture-of-experts architecture may introduce additional computational complexity, which could be a challenge for deployment in resource-constrained environments.

Expert Commentary

The proposed approach represents a significant advancement in the field of large language models, offering a novel solution to the challenges of fine-grained capability distinctions and subtle differences across diverse tasks. While there are limitations to the approach, such as limited generalizability and computational complexity, the results are promising and warrant further investigation. The implications of this research are far-reaching, with potential applications in areas such as AI-powered task automation, natural language processing, and decision support systems. As the field continues to evolve, it is essential to stay ahead of the curve and invest in research and development to ensure that we can harness the full potential of these technologies.

Recommendations

  • ✓ Future research should focus on evaluating the proposed approach in a broader range of domains and tasks to assess its generalizability and scalability.
  • ✓ Investigating ways to reduce computational complexity and improve deployment efficiency in resource-constrained environments is crucial for widespread adoption.

Sources

Original: arXiv - cs.CL

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