arXiv AI
3d ago

On the (In)effectiveness of AMR Augmentation for Large Language Models

The paper investigates whether adding Abstract Meaning Representation (AMR) data to large language models (LLMs) improves performance on downstream tasks. By reproducing recent studies and applying a consistent hyperparameter protocol, the authors find that text-only baselines match or surpass AMR-augmented models. A perplexity-based probe shows that AMR does not provide LLMs with additional relational knowledge, suggesting no clear benefit from AMR augmentation.

By Hoa Quynh Nhung Nguyen, Jacopo Staiano, Michael Sullivan