RouteLLM Paper

Paper Link : https://arxiv.org/pdf/2406.18665v2

Paper Title: RouteLLM: Learning to Route LLMs with Preference Data

With the growing capabilities of large language models (LLMs), efficiently utilizing them becomes crucial. LLM routing emerges as a promising solution. It directs user queries to the most suitable LLM based on factors like complexity and domain. This approach aims to optimize response quality while minimizing costs. However, optimal routing presents a challenge: the router model needs to understand the query’s intent, complexity, and domain, along with the capabilities of candidate LLMs. Additionally, it should be economical, fast, and adaptable to new, improved models.

RouteLLM Framework

This paper proposes a framework for training router models using human preference data and data augmentation techniques. The objective is to minimize costs while achieving a specific performance target. This is achieved by intelligently routing simpler queries to less expensive, “weaker” models and reserving complex queries for “stronger” models. The framework is evaluated on established benchmarks, demonstrating significant cost reductions (over 2x) without sacrificing response quality.

Comparison with Existing Approaches

LLM Routing differs from existing methods like FrugalGPT and LLM-Blender in its routing strategy.

1. FrugalGPT: Uses an LLM cascade method, sequentially querying multiple LLMs until a reliable response is found. This means that the inference cost increases with the number of models involved.
2. LLM-Blender: Utilizes an ensemble framework that calls multiple LLMs at inference and employs a router model to select the best response. Similar to FrugalGPT, the cost grows with the number of models queried.

In contrast, the LLM Routing framework routes each query to a single LLM, which can lead to significant cost savings. It leverages human preference labels from Chatbot Arena and explores several router architectures, showing significant performance improvements through dataset augmentation. Additionally, it emphasizes out-of-domain generalization by evaluating on multiple public benchmarks, which is not a focus in FrugalGPT and LLM-Blender.

They also compared with Hybrid-LLM approach. It is distinct from Hybrid-LLM approach in several key ways:

1. Data Source for Training: The Hybrid-LLM approach uses synthetic preference labels derived via BARTScore, whereas the described approach uses human preference labels from Chatbot Arena.
2. Router Architecture: Hybrid-LLM relies on a single BERT-based router architecture. In contrast, the described approach explores several router architectures.
3. Evaluation Scope: Hybrid-LLM limits its evaluation to in-domain generalization. The described approach emphasizes out-of-domain generalization by evaluating on multiple public benchmarks.

Key Considerations for Cost-Effective Deployment

The paper introduces two key metrics for optimizing the cost-performance trade-off:

• Average Performance Gap Recovered (APGR): This metric indicates how well the router can recover the performance gap between the weak and strong models under cost constraints. A higher APGR signifies effective balancing of performance and cost.
• Call-Performance Threshold (CPT): This metric measures cost-effectiveness. A lower CPT indicates achieving a desired performance level with minimal usage of the expensive strong model. For example, CPT(50%) ≈ 37% indicates that only 37% of the calls need to be routed to the strong model to achieve 50% performance, which is cost-effective.

Cost Analysis and Efficiency

The paper focuses on cost savings compared to directly using the strong model (e.g., GPT-4). Key points include:

• Cost Savings: The proposed routers achieve significant cost savings compared to GPT-4. For instance, the best router achieves up to 3.66 times cost savings on the MT Bench while maintaining 95% of GPT-4’s quality.
• Cost Estimates: The analysis considers GPT-4’s cost (estimated at 0.24 per million tokens) as the dominant factor. Cost savings are calculated based on the reduction in GPT-4 calls required by the router compared to a random baseline.
• Routing Overhead: The paper acknowledges the overhead costs of deploying routers. It details the cost and inference overhead for various router architectures. However, it concludes that these costs are relatively insignificant compared to LLM generation costs, making routing practical for real-world applications.
• Quality-Cost Trade-off: The paper introduces metrics like PGR (performance gap recovered) and APGR (average performance gap recovered) to quantify the router’s performance in balancing quality and cost.

Conclusion

The RouteLLM framework demonstrates that intelligent routing can significantly reduce costs for LLM deployments while maintaining high response quality. This makes it a valuable solution for cost-effective utilization of large language models.