Recent large language models (LLMs) have shown remarkable performance in aligning generated text with user intentions across various tasks. When it comes to long-form text generation, there has been a growing interest in generation from a discourse coherence perspective. However, existing lexical or semantic metrics such as BLEU, ROUGE, BertScore cannot effectively capture the discourse coherence. The development of discourse-specific automatic evaluation methods for assessing the output of LLMs warrants greater focus and exploration. In this paper, we present a novel automatic metric designed to quantify the discourse divergence between two long-form articles. Extensive experiments on three datasets from representative domains demonstrate that our metric aligns more closely with human preferences and GPT-4 coherence evaluation, outperforming existing evaluation methods.
@article{arxiv.2402.10175,
title = {Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence},
author = {Yinhong Liu and Yixuan Su and Ehsan Shareghi and Nigel Collier},
journal= {arXiv preprint arXiv:2402.10175},
year = {2024}
}