ARGENT: Automatic Reference-free Evaluation for Open-Ended Text Generation without Source Inputs

AbstractWith increased accessibility of machine-generated texts, the need for their evaluation has also grown. There are broadly two types of text generation tasks. In open-ended generation tasks (OGTs), the model generates de novo text without any input on which to base it, such as story generation. In reflective generation tasks (RGTs), the model output is generated to reflect an input sequence, such as in machine translation. There are many studies on RGT evaluation, where the metrics typically compare one or more gold-standard references to the model output. Evaluation of OGTs has received less attention and is more challenging: since the task does not aim to reflect an input, there are usually no reference texts. In this paper, we propose a new perspective that unifies OGT evaluation with RGT evaluation, based on which we develop an automatic, reference-free generative text evaluation model (ARGENT), and review previous literature from this perspective. Our experiments demonstrate the effectiveness of these methods across informal, formal, and domain-specific texts. We conduct a meta-evaluation to compare existing and proposed metrics, finding that our approach aligns more closely with human judgement.