Diverse LLMs or Diverse Question Interpretations? That is the Ensembling Question

TL;DR

This paper compares two diversity strategies—model diversity and question interpretation diversity—for ensemble answering with large language models, finding that question interpretation diversity yields more accurate results.

Contribution

It provides an empirical comparison of diversity approaches in LLM ensembles, highlighting the effectiveness of question interpretation diversity over model diversity.

Findings

Question interpretation diversity outperforms model diversity in ensemble accuracy.

Model diversity results are often between the best and worst individual models.

Ensemble methods using question framing improve answer accuracy across datasets.

Abstract

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.