Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Rashkin, Hannah, Clark, Elizabeth, Huot, Fantine, Lapata, Mirella
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866909698440036352
author Rashkin, Hannah
Clark, Elizabeth
Huot, Fantine
Lapata, Mirella
author_facet Rashkin, Hannah
Clark, Elizabeth
Huot, Fantine
Lapata, Mirella
contents Can LLMs provide support to creative writers by giving meaningful writing feedback? In this paper, we explore the challenges and limitations of model-generated writing feedback by defining a new task, dataset, and evaluation frameworks. To study model performance in a controlled manner, we present a novel test set of 1,300 stories that we corrupted to intentionally introduce writing issues. We study the performance of commonly used LLMs in this task with both automatic and human evaluation metrics. Our analysis shows that current models have strong out-of-the-box behavior in many respects -- providing specific and mostly accurate writing feedback. However, models often fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback.
format Preprint
id arxiv_https___arxiv_org_abs_2507_16007
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback
Rashkin, Hannah
Clark, Elizabeth
Huot, Fantine
Lapata, Mirella
Computation and Language
Can LLMs provide support to creative writers by giving meaningful writing feedback? In this paper, we explore the challenges and limitations of model-generated writing feedback by defining a new task, dataset, and evaluation frameworks. To study model performance in a controlled manner, we present a novel test set of 1,300 stories that we corrupted to intentionally introduce writing issues. We study the performance of commonly used LLMs in this task with both automatic and human evaluation metrics. Our analysis shows that current models have strong out-of-the-box behavior in many respects -- providing specific and mostly accurate writing feedback. However, models often fail to identify the biggest writing issue in the story and to correctly decide when to offer critical vs. positive feedback.
title Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback
topic Computation and Language
url https://arxiv.org/abs/2507.16007