Fusion-Eval: Integrating Evaluators with LLMs
Authors: name: Lei Shu, name: Nevan Wichers, name: Liangchen Luo, name: Yun Zhu, name: Yinxiao Liu, name: Jindong Chen, name: Lei Meng

Evaluating Large Language Models (LLMs) is a complex task, especially
considering the intricacies of natural language understanding and the
expectations for high-level reasoning. Traditional evaluations typically lean
on human-based, model-based, or automatic-metrics-based paradigms, each with
its own advantages and shortcomings. We introduce "Fusion-Eval", a system that
employs LLMs not solely for direct evaluations, but to skillfully integrate
insights from diverse evaluators. This gives Fusion-Eval flexibility, enabling
it to work effectively across diverse tasks and make optimal use of multiple
references. In testing on the SummEval dataset, Fusion-Eval achieved a Spearman
correlation of 0.96, outperforming other evaluators. The success of Fusion-Eval
underscores the potential of LLMs to produce evaluations that closely align
human perspectives, setting a new standard in the field of LLM evaluation.