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    <abstract>While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit—or lack thereof—of AMR augmentation for modern LLMs is thus far unclear. In this paper, we attempt to reproduce recent work that reported substantial downstream gains from AMR augmentation, finding that these are likely due to specific choices in the experimental settings used: using a consistent and unified protocol for hyperparameter selection, we observe that text-only baselines consistently match or exceed the performance of AMR-augmented models. To investigate this null result, we introduce a perplexity-based probe measuring the degree to which AMR provides an LLM with supplemental relational knowledge not already available to the model. We find that AMR augmentation does not help LLMs improve their understanding of relational content in the sentence, indicating that augmenting these models with AMR offers no clear benefit on downstream tasks.</abstract>
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%0 Conference Proceedings
%T On the (In)effectiveness of AMR Augmentation for Large Language Models
%A Nguyen, Hoa Quynh Nhung
%A Staiano, Jacopo
%A Sullivan, Michael
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F nguyen-etal-2026-effectiveness
%X While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit—or lack thereof—of AMR augmentation for modern LLMs is thus far unclear. In this paper, we attempt to reproduce recent work that reported substantial downstream gains from AMR augmentation, finding that these are likely due to specific choices in the experimental settings used: using a consistent and unified protocol for hyperparameter selection, we observe that text-only baselines consistently match or exceed the performance of AMR-augmented models. To investigate this null result, we introduce a perplexity-based probe measuring the degree to which AMR provides an LLM with supplemental relational knowledge not already available to the model. We find that AMR augmentation does not help LLMs improve their understanding of relational content in the sentence, indicating that augmenting these models with AMR offers no clear benefit on downstream tasks.
%U https://aclanthology.org/2026.emnlp-main.754/
%P 17428-17450

Markdown (Informal)

[On the (In)effectiveness of AMR Augmentation for Large Language Models](https://aclanthology.org/2026.emnlp-main.754/) (Nguyen et al., EMNLP 2026)

• On the (In)effectiveness of AMR Augmentation for Large Language Models (Nguyen et al., EMNLP 2026)

ACL

• Hoa Quynh Nhung Nguyen, Jacopo Staiano, and Michael Sullivan. 2026. On the (In)effectiveness of AMR Augmentation for Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 17428–17450, Budapest, Hungary. Association for Computational Linguistics.