Routing to the Expert: Efficient Reward-guided Ensemble of Large
  Language Models
Keming Lu Hongyi Yuan Runji Lin Junyang Lin Zheng Yuan Chang Zhou Jingren Zhou
The complementary potential of Large Language Models (LLM) assumes
off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and
tasks so that an ensemble of LLMs can achieve consistently better performance.
Existing ensemble methods for LLMs mainly focus on reward model ranking of
outputs, leading to significant computation overhead. To combat this issue, we
revisit the complementary potential of LLMs and further elaborate it by mining
latent expertise with off-the-shelf reward models. We propose Zooter, a
reward-guided routing method distilling rewards on training queries to train a
routing function, which can precisely distribute each query to the LLM with
expertise about it. We also integrate a tag-based label enhancement to mitigate
noise from uncertainty when using rewards as silver supervision. Zooter shows
computation efficiency in inference as it introduces only a minor computation
overhead of a routing function compared with reward model ranking methods. We
evaluate Zooter on a comprehensive benchmark collection with 26 subsets on
different domains and tasks. Zooter outperforms the best single model on
average and ranks first on 44% of tasks, even surpassing multiple reward model
ranking methods.