Problem
Research questions and friction points this paper is trying to address.
Abstract Meaning Representation
Large Language Models
AMR Augmentation
Downstream Tasks
On the (In)effectiveness of AMR Augmentation for Large Language Models
🤖 AI Summary
This study investigates whether Abstract Meaning Representation (AMR) augmentation can substantially enhance the downstream task performance of large language models (LLMs). To this end, we establish a unified hyperparameter protocol to reproduce and compare fine-tuning experiments, and propose a novel perplexity-based probing method to quantitatively assess whether AMR supplies relational knowledge absent from the model. Rigorous controlled experiments reveal that plain-text baselines consistently outperform AMR-augmented models. Probing analysis further elucidates this failure mechanism: modern LLMs have already internalized the requisite relational understanding, rendering AMR incapable of providing meaningful supplementary information. Ultimately, this work clarifies the practical utility boundaries of structured semantic augmentation for contemporary LLMs.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Abstract Meaning Representation
Large Language Models
Perplexity-based Probe
AMR Augmentation
Relational Knowledge
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