Skip to content

SR-LLM: Rethinking the Structured Representation in Large Language Model

Conference: ACL 2025
arXiv: 2502.14352
Code: None
Area: NLP / Structured Representation
Keywords: Abstract Meaning Representation, Structured Information, LLM Enhancement, Natural Language Description, Fine-tuning

TL;DR

This work proposes the SR-LLM framework, which effectively integrates structured representations (AMR, PST, FOL) into LLMs through two settings: training-free natural language description conversion and training-dependent hybrid-data fine-tuning. It achieves performance improvements of 3.17% and 12.38% respectively on downstream tasks like PAWS, providing the first substantial evidence that structured representations can enhance the reasoning capabilities of LLMs.

Background & Motivation

Structured representations (such as Abstract Meaning Representation (AMR), Phrase Structure Trees (PST), and First-Order Logic (FOL)) are crucial in traditional NLP, but their roles have become ambiguous in the LLM era:

  1. Limitations of Prior Work (Direct Integration is Detrimental): Prior work (such as AMRCOT) directly incorporated structures like AMR in code format into prompts, which unexpectedly resulted in performance degradation (\(-5.18\%\) on PAWS), casting doubt on the value of structured representations in LLMs.
  2. Encoding Format Mismatch Hypothesis: The authors hypothesize that the degradation is due to structured information being presented in code formats uncommon in LLM pre-training corpora, rather than the structured information itself being useless.
  3. Lack of Effective Integration Paradigm: How to optimally utilize structured representations in the LLM era remains an open challenge.

Core Idea: Convert structured representations into natural language descriptions that LLMs are more familiar with, enabling models to truly comprehend and utilize structured information.

Method

Overall Architecture

SR-LLM consists of two configurations: (1) Training-Free: utilizing the SR-to-NLD module to convert structured representations into natural language descriptions, which are directly incorporated into the prompt; (2) Training-Dependent: constructing a hybrid dataset Gen-SR containing structured representations for SFT (Supervised Fine-Tuning) to establish an internal correlation between the tasks and structured information.

Key Designs

  1. SR-to-NLD (Structured Representation to Natural Language Description): Taking AMR as an example, this process consists of four phases: (Phase 0) utilizing the Penman library to convert the AMR graph into triples; (Phase 1) identifier instantiation, replacing abstract identifiers with concrete concepts; (Phase 2) mapping triples to natural language sentences using predefined rule dictionaries; (Phase 3) refining the generated descriptions using GPT-4o Mini to ensure fluency and coherence, and applying a voting mechanism over multiple generations to reduce hallucinations. The Design Motivation is to present natural language forms common in LLM pre-training corpora rather than unfamiliar code formats. Distinct from traditional SR-to-Text (which generates a single complete sentence), SR-to-NLD describes structural information through multi-sentence collaboration.

  2. Gen-SR Hybrid Data Fine-Tuning: Consists of two components—G(text), which contains instruction pairs of raw text only, and G(SR), which includes structured representations within the instruction pairs. Joint training is performed using a hybrid ratio of 50% G(text) + 50% G(SR). The Design Motivation is to enable the model to not only learn downstream tasks but also establish internal correlations between tasks and structural information, allowing for more effective utilization of structured information during inference.

  3. Support for Multiple Structured Representations: The framework supports three types of SR—AMR (Abstract Meaning Representation), PST (Phrase Structure Tree), and FOL (First-Order Logic), capturing semantic, syntactic, and logical information respectively. Each type of SR has a corresponding NLD conversion method.

Loss & Training

  • The Training-Free setting requires no training and directly appends the SR-NLD to the prompt.
  • The Training-Dependent setting performs SFT utilizing Llama3.1-8B-Instruct.
  • Joint training is conducted across data from 10 tasks (rather than specialized for a single task).
  • Experiments are conducted independently on the three types of SR (AMR, PST, FOL), with results averaged.
  • Utilizes both CoT and One-Shot prompting strategies.

Key Experimental Results

Main Results (Training-Free, Llama3.1-8b-Instruct)

Dataset Metric Original Prompt + SR Code Format + SR-NLD (Ours) Gain
PAWS F1 41.59 36.41 (-5.18) 44.77 (+3.17) +8.36 vs SR
Logic F1 15.48 14.20 18.27 (+2.79) +4.07 vs SR
AGNEWS F1 53.88 48.17 56.67 (+2.79) +8.50 vs SR
WiC F1 43.99 42.05 48.17 (+4.18) +6.12 vs SR
SPIDER F1 24.80 21.53 29.60 (+4.80) +8.07 vs SR

Main Results (Training-Dependent, Llama3.1-8b-Instruct)

Fine-Tuning Strategy PAWS (F1) SNLI (F1) WiC (F1) SST-2 (F1) SPIDER (EM)
100% G(text) 68.94 35.53 66.97 75.59 41.20
100% G(SR) + SR prompt 75.39 56.62 70.82 81.62 40.60
50% G(SR) + 50% G(text) + SR prompt 81.04 54.92 74.68 83.72 48.93

Ablation Study

Configuration Key Metrics Explanation
AMRBART generated AMR vs GPT-4o Difference <1% The source of SR has minimal impact on performance
Flawed AMR vs Gold AMR Significant difference (4-9%) SR quality is crucial
Gold AMR + NLD Best performance NLD conversion amplifies the benefits of high-quality SR
Different hybrid ratios 50-50 is optimal Balancing text and SR is essential

Key Findings

  • Integrating SR as natural language descriptions consistently outperforms code formats, validating the encoding format mismatch hypothesis.
  • Weaker models benefit more from structured information: Llama3.1-8b shows significant and consistent improvements, while GPT-4o-mini shows minimal gains.
  • Training-Dependent setup yields a massive improvement of \(+12.38\%\) on PAWS (increasing from 68.66 to 81.04).
  • SR quality matters: low-quality or flawed AMRs significantly impair performance (\(-4\%\) to \(-9\%\)), whereas high-quality AMR-NLD dramatically enhances results.
  • SR can only be effectively utilized during inference after the correlation between SR and tasks is established in the training data.
  • Training solely on SR data is inferior to raw text and SR hybrid training.

Highlights & Insights

  • First positive proof that SR enhances LLMs: This reverses the assumption that "structured representation is useless in the LLM era," showing that the core issue lies in the representation format rather than the information itself.
  • Natural language as the universal interface for LLMs: No matter how abstract the structured information is, once converted into natural language descriptions, it can be effectively processed and ingested by LLMs.
  • Weaker models benefit more: Structured information serves as an effective supplement for models that lack robust intrinsic reasoning capabilities, whereas it shows diminishing marginal utility for stronger models.
  • Importance of hybrid training: Training on purely structured data yields worse results than hybrid training, as models need to simultaneously maintain understanding of both raw text and structures.

Limitations & Future Work

  • SR generation relies on external tools (AMR parser / GPT-4o), introducing additional computational overhead and potential errors.
  • The use of GPT-4o Mini for refinement in NLD conversion may introduce costs and uncertainty.
  • The coverage of 10 tasks is limited; validation on generative tasks (such as summarization and dialogue) was not conducted.
  • The cost of training-dependent setups was not analyzed in detail.
  • Differences in task applicability across various SR types (AMR vs. PST vs. FOL) were not analyzed in depth.
  • Contrast with AMRCOT (Jin et al., 2024): while both utilize AMR, the different representation formats led to opposite results.
  • The integration of structured knowledge with LLMs is an important trend, and this paper provides a new perspective on "how to integrate" them.
  • Insight: Structured information such as knowledge graphs and tabular data can also try to be integrated into LLMs after NLD conversion.
  • The finding that weaker models benefit more has guiding significance for enhancement strategies post-model compression.

Rating

Dimension Score (1-5) Explanation
Novelty 4 Simple yet effective insight: format rather than content is the bottleneck.
Practicality 3 The NLD conversion workflow is reusable, but relies on external tools.
Experimental Thoroughness 4 Comprehensive evaluation across 10 tasks, 3 models, and multiple SR types.
Writing Quality 3 Clear structure but loaded with notation, dense in places.
Overall Score 3.5 Valuable insights that dismantle the myth of SR being useless in the LLM era.