Exploring the Feasibility of Efficiently Routing Queries to Diverse Large Language Models

Core Concepts

Investigating whether directing input prompts to the most suitable single large language model can lead to better performance than individual models while maintaining reasonable latency.

Abstract

The paper explores the feasibility of LLM routing, which aims to efficiently select the most suitable single large language model (LLM) from a pool of diverse LLMs to solve a given input query. The authors focus on two challenging reasoning task benchmarks - GSM8K and MMLU - and experiment with 7 open-source LLMs.

The key aspects of the study are:

1. LLM Sampling: The authors select diverse LLMs based on criteria such as performance, training methodologies, and model specialization. They generate 10 responses per input query to ensure reliable and replicable behavior.

2. LLM Routing Approaches:

   • Classifier-based Routing: The authors explore multi-label and separate classifiers to predict the set of LLMs capable of solving each input query, along with confidence scores. They design various policies to select the optimal single LLM based on the confidence scores.
   • Clustering-based Routing: The authors fit a K-Means clustering model on query-level features to learn discrete clusters, and then route each test query to the best-performing LLM for its corresponding cluster.

3. Evaluation and Analysis:

   • The authors introduce theoretical upper bounds for the routing model's performance, considering the highest possible performance achieved jointly with all LLMs (oracle) and the highest performance achieved with the proposed routing model.
   • They compare the performance of the routing models with individual LLMs and various baselines, including random selection and joint performance of all LLMs.
   • The authors analyze the impact of different routing policies, the effectiveness of multi-label vs. separate classifiers, and the clustering-based approach.
   • They also discuss the inference latency of the routing models and compare it to individual LLMs.

The key findings suggest that while the theoretical upper bounds of the routing model are higher than individual model performance, the practical routing model developed is unable to achieve those scores, primarily due to the limited training data. The performance of the routing model is better than weak LLMs but similar to or slightly lower than the top-performing LLMs. The authors conclude that LLM routing is a promising direction that requires further research, such as collecting larger datasets and developing novel routing models.

Stats

The GSM8K dataset contains 8,792 diverse grade-school level math word problems.
The MMLU dataset contains 15,000 multiple-choice questions spanning 57 subjects across STEM, humanities, and social sciences.
The mean accuracy (MAJ@10) of individual LLMs ranges from 36.84% to 71.11% on GSM8K and from 42.28% to 63.85% on MMLU.
The theoretical upper bound (oracle) accuracy is 87.18% for GSM8K and 89.15% for MMLU.
The upper bound accuracy of the proposed routing model is 79.68% for GSM8K and 77.18% for MMLU.

Quotes

"With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently."
"Experiments towards predicting model behavior also suggest that particular aspects of input prompts can affect different LLMs in different ways."
"Our extensive experiments suggest that such routing shows promise but is not feasible in all scenarios, so more robust approaches should be investigated to fill this gap."

Key Insights Distilled From

Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing

by KV Aditya Sr... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00467.pdf

Deeper Inquiries

How can the proposed LLM routing model be extended to handle a larger and more diverse pool of LLMs?

To extend the proposed LLM routing model to handle a larger and more diverse pool of LLMs, several strategies can be implemented:

Data Augmentation: Increasing the training data by augmenting existing datasets or collecting additional data from various sources can help the model learn from a more diverse set of examples.

Model Ensemble: Instead of selecting a single LLM, the routing model can be designed to ensemble multiple LLMs based on their strengths and weaknesses. This can lead to more robust decision-making.

Dynamic Routing: Implementing a dynamic routing mechanism that adapts to the performance of LLMs on specific queries can enhance the model's ability to select the most suitable LLM for each input.

Transfer Learning: Leveraging pre-trained models and fine-tuning them on domain-specific tasks can enable the routing model to handle a wider range of LLMs with varying capabilities.

Regularization Techniques: Incorporating regularization techniques like dropout, weight decay, or early stopping can prevent overfitting and improve the generalization of the routing model across diverse LLMs.

By incorporating these strategies, the LLM routing model can effectively scale to accommodate a larger and more diverse pool of LLMs, enhancing its performance and adaptability.

How can the proposed LLM routing model be extended to handle a larger and more diverse pool of LLMs?

To improve the performance of the routing model, especially when dealing with LLMs with vastly different capabilities, the following novel techniques can be developed:

Adaptive Confidence Scoring: Implementing an adaptive confidence scoring mechanism that dynamically adjusts the confidence thresholds based on the performance of individual LLMs can enhance the model's decision-making process.

Meta-Learning: Introducing meta-learning techniques that enable the routing model to learn how to adapt to different LLM capabilities through experience can improve its ability to select the most suitable LLM for each query.

Attention Mechanisms: Incorporating attention mechanisms that focus on specific features or patterns in the input queries can help the model better understand the nuances of different LLMs and make more informed routing decisions.

Model Calibration: Developing calibration techniques to calibrate the output probabilities of the routing model can improve its reliability and accuracy in selecting the most appropriate LLM for each input.

Adversarial Training: Employing adversarial training to expose the routing model to challenging scenarios and diverse LLM capabilities can enhance its robustness and decision-making under varying conditions.

By implementing these novel techniques, the performance of the routing model can be significantly enhanced, especially when dealing with LLMs with vastly different capabilities, leading to more accurate and efficient routing decisions.

How can the proposed LLM routing approach be applied to other domains beyond reasoning tasks, such as language generation or translation, to further explore its potential and limitations?

The proposed LLM routing approach can be applied to other domains beyond reasoning tasks, such as language generation or translation, by adapting the model architecture and training process to suit the specific requirements of these tasks:

Language Generation: For language generation tasks, the routing model can be trained to select the most suitable LLM based on the desired style, tone, or content of the generated text. This can help in producing more coherent and contextually relevant outputs.

Translation: In the context of translation, the routing model can be designed to choose the LLM that is most proficient in translating specific language pairs or handling domain-specific terminology. This can improve the accuracy and fluency of translated texts.

Summarization: When applied to summarization tasks, the routing model can select the LLM that excels in condensing information while preserving key details and context. This can lead to more concise and informative summaries.

Dialogue Systems: For dialogue systems, the routing model can be used to select the LLM that is best suited for generating responses based on the context of the conversation, user preferences, and task requirements. This can enhance the conversational quality and engagement.

By adapting the LLM routing approach to these domains, researchers can further explore its potential in enhancing various natural language processing tasks and uncover any limitations or challenges that may arise when dealing with different types of language models and tasks.