Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question
This work addresses the challenge of effectively leveraging diversity in LLMs for question answering, providing insights for practitioners on ensemble strategies.
The paper compared two diversity approaches for binary question answering with LLMs: model diversity (multiple models) versus question interpretation diversity (multiple question framings), finding that question interpretation diversity consistently achieved higher ensemble accuracy across three datasets.
Effectively leveraging diversity has been shown to improve performance for various machine learning models, including large language models (LLMs). However, determining the most effective way of using diversity remains a challenge. In this work, we compare two diversity approaches for answering binary questions using LLMs: model diversity, which relies on multiple models answering the same question, and question interpretation diversity, which relies on using the same model to answer the same question framed in different ways. For both cases, we apply majority voting as the ensemble consensus heuristic to determine the final answer. Our experiments on boolq, strategyqa, and pubmedqa show that question interpretation diversity consistently leads to better ensemble accuracy compared to model diversity. Furthermore, our analysis of GPT and LLaMa shows that model diversity typically produces results between the best and the worst ensemble members without clear improvement.