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arxiv: 2504.04745 · v4 · submitted 2025-04-07 · 💻 cs.CL

Can LLMs Interpret and Leverage Structured Linguistic Representations? A Case Study with AMRs

Ankush Raut , Xiaofeng Zhu , Maria Leonor Pacheco This is my paper

Pith reviewed 2026-05-22 20:50 UTC · model grok-4.3

classification 💻 cs.CL
keywords large language modelsabstract meaning representationcontext augmentationdialogue summarizationSAMSumperformance evaluation
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The pith

AMR structures improve LLM performance on long-context tasks like dialogue summarization but often degrade it on short-context tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests whether Large Language Models can draw on Abstract Meaning Representations, which are graph structures that capture sentence meaning, when the graphs are added to prompts. The authors run experiments on models including Llama 3.1, Phi-3, and Mistral across several language tasks, comparing performance with and without the added AMR graphs. On short contexts the added structure tends to lower results, yet on long-context dialogue summarization it raises zero-shot cosine similarity, for instance lifting Llama 3.1 from 66 percent to 76 percent. Gains are larger in newer and bigger models. The same models can also turn linearized AMRs back into the original text at up to 81 percent similarity.

Core claim

The central claim is that augmenting prompts with linearized AMR structures improves LLM output on long-context tasks such as SAMSum dialogue summarization while typically reducing performance on short-context tasks, with the benefit clearest in larger and more recent models, and that LLMs can reconstruct original text from linearized AMR at cosine similarity up to 81 percent.

What carries the argument

Abstract Meaning Representation (AMR), a graph that encodes the core meaning of text; it is linearized and inserted into the prompt to supply structured semantic context to the LLM.

If this is right

  • The improvement from AMR grows with model size and recency.
  • LLMs can recover original sentences from linearized AMR at high similarity.
  • AMR augmentation helps only when the original context is long.
  • Short-context tasks receive no benefit or a penalty from the added structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • AMR may supply a compact semantic scaffold that helps models track relations across many turns of dialogue.
  • The same linearization technique could be tried with other graph or tree representations to test whether the benefit is specific to AMR.
  • Reconstruction success suggests AMRs could serve as a compressed alternative input format for very long documents.

Load-bearing premise

The performance shifts are produced by the AMR graph structure itself rather than by other details of prompt wording or the way the graphs are turned into text.

What would settle it

No rise in cosine similarity on SAMSum when AMR is added to long-context prompts after the total number of tokens in the prompt is held constant.

read the original abstract

This paper evaluates the ability of Large Language Models (LLMs) to leverage contextual information in the form of structured linguistic representations. Specifically, we examine the impact of encoding both short and long contexts using Abstract Meaning Representation (AMR) structures across a diverse set of language tasks. We perform our analysis using 8-bit quantized and instruction-tuned versions of Llama 3.1 (8B), Phi-3, and Mistral 7B. Our results indicate that, for tasks involving short contexts, augmenting the prompt with the AMR of the original language context often degrades the performance of the underlying LLM. However, for tasks that involve long contexts, such as dialogue summarization in the SAMSum dataset, this enhancement improves LLM performance, for example, by increasing the zero-shot cosine similarity score of Llama 3.1 from 66% to 76%. This improvement is more evident in the newer and larger LLMs, but does not extend to the older or smaller ones. In addition, we observe that LLMs can effectively reconstruct the original text from a linearized AMR, achieving a cosine similarity of 81% in the best-case scenario.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. This paper evaluates whether LLMs can interpret and leverage Abstract Meaning Representations (AMRs) by augmenting prompts with linearized AMRs on short- and long-context language tasks. Using 8-bit quantized instruction-tuned Llama 3.1 (8B), Phi-3, and Mistral 7B, the authors report that AMR augmentation degrades performance on short-context tasks but improves it on long-context tasks such as dialogue summarization on SAMSum (e.g., Llama 3.1 zero-shot cosine similarity rising from 66% to 76%). They also report that LLMs can reconstruct original text from linearized AMRs with up to 81% cosine similarity in the best case.

Significance. If the performance gains on long-context tasks can be attributed specifically to AMR's predicate-argument structure rather than prompt length or formatting, the results would suggest a practical way to inject semantic structure for better long-context handling in LLMs. The reconstruction finding provides some evidence of AMR interpretability. The differential short-vs-long effect is potentially useful for prompting strategies, but current evidence is preliminary due to missing controls.

major comments (3)
  1. [Results / SAMSum experiments] The headline comparison (plain text vs. AMR-augmented prompts) does not isolate the contribution of AMR's structured predicate-argument relations from increased input length or the specific linearization syntax. The reported SAMSum gains (Llama 3.1: 66% → 76% zero-shot cosine similarity) therefore cannot yet be confidently attributed to the AMR representation itself rather than confounds. This is load-bearing for the central claim that LLMs leverage the structured linguistic representation.
  2. [Experimental setup / Abstract and Results sections] Experimental setup details are missing or insufficiently described: the number of evaluation examples, the AMR parser used to obtain the structures, the exact prompt templates and linearization format, and any statistical significance tests for the reported differences are not provided. This makes it difficult to assess reproducibility and reliability of the short-context degradation and long-context improvement claims.
  3. [Reconstruction analysis] For the text-reconstruction experiment, the 81% cosine similarity figure lacks a clear baseline comparison (e.g., against random or length-matched strings) and details on how the linearized AMR is generated and fed to the model, weakening the supporting claim that LLMs can effectively interpret AMRs.
minor comments (2)
  1. [Evaluation metrics] Clarify whether additional summarization metrics (ROUGE, BERTScore, etc.) were computed for SAMSum alongside cosine similarity, and report them if available.
  2. [Task descriptions] Specify the exact context lengths or token counts for the 'short' vs. 'long' context tasks to make the differential effect more interpretable.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment point by point below, agreeing where revisions are warranted and providing clarifications or defenses on points of substance.

read point-by-point responses

  1. Referee: [Results / SAMSum experiments] The headline comparison (plain text vs. AMR-augmented prompts) does not isolate the contribution of AMR's structured predicate-argument relations from increased input length or the specific linearization syntax. The reported SAMSum gains (Llama 3.1: 66% → 76% zero-shot cosine similarity) therefore cannot yet be confidently attributed to the AMR representation itself rather than confounds. This is load-bearing for the central claim that LLMs leverage the structured linguistic representation.

    Authors: We acknowledge that the direct comparison does not fully disentangle AMR's predicate-argument structure from confounds such as increased token length or the linearization syntax. In the revision we will add control conditions (e.g., length-matched non-semantic strings and alternative non-AMR formats) to better isolate the contribution. At the same time, the opposite directional effects observed on short-context versus long-context tasks already argue against a purely length-based account, since any uniform length penalty would not produce degradation on short tasks and improvement on long tasks. revision: partial

  2. Referee: [Experimental setup / Abstract and Results sections] Experimental setup details are missing or insufficiently described: the number of evaluation examples, the AMR parser used to obtain the structures, the exact prompt templates and linearization format, and any statistical significance tests for the reported differences are not provided. This makes it difficult to assess reproducibility and reliability of the short-context degradation and long-context improvement claims.

    Authors: We agree these details should have been included. The revised manuscript will report the exact number of evaluation examples per task, name the AMR parser, reproduce the prompt templates and linearization format (in a new appendix), and add statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) for the key differences. revision: yes

  3. Referee: [Reconstruction analysis] For the text-reconstruction experiment, the 81% cosine similarity figure lacks a clear baseline comparison (e.g., against random or length-matched strings) and details on how the linearized AMR is generated and fed to the model, weakening the supporting claim that LLMs can effectively interpret AMRs.

    Authors: We accept that explicit baselines would strengthen the interpretability claim. We will add comparisons to random strings and length-matched controls in the revised reconstruction section and will expand the description of how the linearized AMR is produced and presented to the model. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation of LLM performance

full rationale

The paper conducts direct experiments measuring LLM outputs (cosine similarity, task performance) on standard datasets with and without AMR-augmented prompts. No equations, parameter fitting, derivations, or uniqueness theorems are present. All reported improvements (e.g., Llama 3.1 on SAMSum) are measured against external benchmarks rather than derived from the paper's own inputs. No self-citation chains or ansatzes are used to justify core claims. This is a standard empirical study whose results stand or fall on the reported measurements.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper depends on standard assumptions in NLP about representation formats and evaluation metrics, with no new entities postulated.

axioms (2)
  • domain assumption AMR graphs can be linearized into text that LLMs can process as input
    The experiments rely on providing linearized AMR in prompts.
  • domain assumption Cosine similarity on embeddings is a valid measure of performance for the tasks
    Used for evaluation in summarization and reconstruction.

pith-pipeline@v0.9.0 · 5740 in / 1369 out tokens · 51479 ms · 2026-05-22T20:50:31.104808+00:00 · methodology

discussion (0)

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