@inproceedings{wang-etal-2026-feedback,
title = "Feedback as a Guide: Enhancing {LLM} Comprehension of {M}odern {C}hinese Poetry",
author = "Wang, Shanshan and
Wong, Derek F. and
Luo, Jiebo and
Yao, Jingming and
Chao, Lidia S.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.475/",
pages = "9965--9986",
ISBN = "979-8-89176-462-0",
abstract = "The performance of large language models (LLMs) on poetry-related tasks has drawn increasing attention from researchers. Since understanding of poetry is fundamental to all poetry-related tasks, enhancing LLMs' comprehension of poetry is crucial for advancing this field. In this study, we propose a novel method to improve LLMs' comprehension of modern Chinese poetry. This method utilizes feedback from reliable LLM evaluators as guidance to enhance other LLMs' understanding of poetry in specific dimensions. To identify reliable LLM evaluators, we evaluate the initial performance of several mainstream LLMs on poetry comprehension tasks and further refine their evaluation capabilities until they approach human (expert) performance. These reliable LLMs are then employed as evaluators to simulate poet{'}s perspective in assessing the poetry comprehension of other LLMs, providing reasons and suggestions for improvement. Experimental results show that our method effectively enhances LLMs' comprehension of poetry across multiple dimensions."
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<abstract>The performance of large language models (LLMs) on poetry-related tasks has drawn increasing attention from researchers. Since understanding of poetry is fundamental to all poetry-related tasks, enhancing LLMs’ comprehension of poetry is crucial for advancing this field. In this study, we propose a novel method to improve LLMs’ comprehension of modern Chinese poetry. This method utilizes feedback from reliable LLM evaluators as guidance to enhance other LLMs’ understanding of poetry in specific dimensions. To identify reliable LLM evaluators, we evaluate the initial performance of several mainstream LLMs on poetry comprehension tasks and further refine their evaluation capabilities until they approach human (expert) performance. These reliable LLMs are then employed as evaluators to simulate poet’s perspective in assessing the poetry comprehension of other LLMs, providing reasons and suggestions for improvement. Experimental results show that our method effectively enhances LLMs’ comprehension of poetry across multiple dimensions.</abstract>
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%0 Conference Proceedings
%T Feedback as a Guide: Enhancing LLM Comprehension of Modern Chinese Poetry
%A Wang, Shanshan
%A Wong, Derek F.
%A Luo, Jiebo
%A Yao, Jingming
%A Chao, Lidia S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-feedback
%X The performance of large language models (LLMs) on poetry-related tasks has drawn increasing attention from researchers. Since understanding of poetry is fundamental to all poetry-related tasks, enhancing LLMs’ comprehension of poetry is crucial for advancing this field. In this study, we propose a novel method to improve LLMs’ comprehension of modern Chinese poetry. This method utilizes feedback from reliable LLM evaluators as guidance to enhance other LLMs’ understanding of poetry in specific dimensions. To identify reliable LLM evaluators, we evaluate the initial performance of several mainstream LLMs on poetry comprehension tasks and further refine their evaluation capabilities until they approach human (expert) performance. These reliable LLMs are then employed as evaluators to simulate poet’s perspective in assessing the poetry comprehension of other LLMs, providing reasons and suggestions for improvement. Experimental results show that our method effectively enhances LLMs’ comprehension of poetry across multiple dimensions.
%U https://aclanthology.org/2026.findings-emnlp.475/
%P 9965-9986
Markdown (Informal)
[Feedback as a Guide: Enhancing LLM Comprehension of Modern Chinese Poetry](https://aclanthology.org/2026.findings-emnlp.475/) (Wang et al., Findings 2026)
ACL