Context is Enough: Empirical Validation of $\textit{Sequentiality}$ on Essays

• URL: http://arxiv.org/abs/2511.09185v1
• Date: Thu, 13 Nov 2025 01:38:21 GMT
• Title: Context is Enough: Empirical Validation of $\textit{Sequentiality}$ on Essays
• Authors: Amal Sunny, Advay Gupta, Vishnu Sreekumar,
• Abstract summary: We show that the contextual version of sequentiality aligns more closely with human assessments of discourse-level traits.<n>Our findings support the use of context-based sequentiality as a validated, interpretable, and complementary feature for automated essay scoring and related NLP tasks.
• Score: 1.338174941551702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has proposed using Large Language Models (LLMs) to quantify narrative flow through a measure called sequentiality, which combines topic and contextual terms. A recent critique argued that the original results were confounded by how topics were selected for the topic-based component, and noted that the metric had not been validated against ground-truth measures of flow. That work proposed using only the contextual term as a more conceptually valid and interpretable alternative. In this paper, we empirically validate that proposal. Using two essay datasets with human-annotated trait scores, ASAP++ and ELLIPSE, we show that the contextual version of sequentiality aligns more closely with human assessments of discourse-level traits such as Organization and Cohesion. While zero-shot prompted LLMs predict trait scores more accurately than the contextual measure alone, the contextual measure adds more predictive value than both the topic-only and original sequentiality formulations when combined with standard linguistic features. Notably, this combination also outperforms the zero-shot LLM predictions, highlighting the value of explicitly modeling sentence-to-sentence flow. Our findings support the use of context-based sequentiality as a validated, interpretable, and complementary feature for automated essay scoring and related NLP tasks.

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