Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence

• URL: http://arxiv.org/abs/2402.10175v2
• Date: Tue, 2 Apr 2024 21:51:36 GMT
• Title: Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence
• Authors: Yinhong Liu, Yixuan Su, Ehsan Shareghi, Nigel Collier,
• Abstract summary: We present a novel metric designed to quantify the discourse divergence between two long-form articles. Our metric aligns more closely with human preferences and GPT-4 coherence evaluation, outperforming existing evaluation methods.
• Score: 39.065349875944634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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