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    <abstract>Abstract Meaning Representation (AMR) is a graph-based semantic representation that has been incorporated into numerous downstream tasks, in particular due to substantial efforts developing text-to-AMR parsing and AMR-to-text generation models. However, there still exists a large gap between fluent, natural sentences and texts generated from AMR-to-text generation models. Prompt-based Large Language Models (LLMs), on the other hand, have demonstrated an outstanding ability to produce fluent text in a variety of languages and domains. In this paper, we investigate the extent to which LLMs can improve the AMR-to-text generated output fluency post-hoc via prompt engineering. We conduct automatic and human evaluations of the results, and ultimately have mixed findings: LLM-generated paraphrases generally do not exhibit improvement in automatic evaluation, but outperform baseline texts according to our human evaluation. Thus, we provide a detailed error analysis of our results to investigate the complex nature of generating highly fluent text from semantic representations.</abstract>
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%0 Conference Proceedings
%T GPT4AMR: Does LLM-based Paraphrasing Improve AMR-to-text Generation Fluency?
%A Ji, Jiyuan
%A Wein, Shira
%Y Zhang, Chen
%Y Allaway, Emily
%Y Shen, Hua
%Y Miculicich, Lesly
%Y Li, Yinqiao
%Y M’hamdi, Meryem
%Y Limkonchotiwat, Peerat
%Y Bai, Richard He
%Y T.y.s.s., Santosh
%Y Han, Sophia Simeng
%Y Thapa, Surendrabikram
%Y Rim, Wiem Ben
%S Proceedings of the 9th Widening NLP Workshop
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-351-7
%F ji-wein-2025-gpt4amr
%X Abstract Meaning Representation (AMR) is a graph-based semantic representation that has been incorporated into numerous downstream tasks, in particular due to substantial efforts developing text-to-AMR parsing and AMR-to-text generation models. However, there still exists a large gap between fluent, natural sentences and texts generated from AMR-to-text generation models. Prompt-based Large Language Models (LLMs), on the other hand, have demonstrated an outstanding ability to produce fluent text in a variety of languages and domains. In this paper, we investigate the extent to which LLMs can improve the AMR-to-text generated output fluency post-hoc via prompt engineering. We conduct automatic and human evaluations of the results, and ultimately have mixed findings: LLM-generated paraphrases generally do not exhibit improvement in automatic evaluation, but outperform baseline texts according to our human evaluation. Thus, we provide a detailed error analysis of our results to investigate the complex nature of generating highly fluent text from semantic representations.
%U https://aclanthology.org/2025.winlp-main.2/
%P 9-18

Markdown (Informal)

[GPT4AMR: Does LLM-based Paraphrasing Improve AMR-to-text Generation Fluency?](https://aclanthology.org/2025.winlp-main.2/) (Ji & Wein, WiNLP 2025)

• GPT4AMR: Does LLM-based Paraphrasing Improve AMR-to-text Generation Fluency? (Ji & Wein, WiNLP 2025)

ACL

• Jiyuan Ji and Shira Wein. 2025. GPT4AMR: Does LLM-based Paraphrasing Improve AMR-to-text Generation Fluency?. In Proceedings of the 9th Widening NLP Workshop, pages 9–18, Suzhou, China. Association for Computational Linguistics.