Can LLMs Interpret and Leverage Structured Linguistic Representations? A Case Study with AMRs¶
Conference: ACL 2025
Code: None
Area: LLM / NLP
TL;DR¶
This study systematically evaluates the capability of LLMs to leverage Abstract Meaning Representation (AMR) in downstream tasks. It is found that AMR-augmented prompts significantly improve the zero-shot performance of Llama 3.1 on long-context tasks such as dialogue summarization (raising cosine similarity from 66% to 76%), whereas they typically degrade performance on short-context tasks.
Background & Motivation¶
- LLMs excel in NLP tasks, but their depth of understanding remains questionable: While LLMs showcase outstanding performance in tasks like translation and summarization, it remains unclear whether they can extract and utilize information from structured semantic representations.
- Effectiveness of AMR has been validated in traditional approaches: Abstract Meaning Representation (AMR) has been proven to effectively enhance reasoning behavior in structure-aware NLP tasks, especially in long-context scenarios.
- Prior work relies on architectural modifications: Most previous attempts to exploit AMR modify model architectures (e.g., text-graph attention, graph Transformers), which increases complexity and impedes generalization.
- Lack of direct evaluation on LLM's understanding of AMR: No prior studies have systematically evaluated the capability of general-purpose LLMs to directly interpret linearized AMRs and how this ability varies across different task types.
- A new direction for prompt engineering: Incorporating structured semantic information into prompts represents a low-cost, architecture-free enhancement strategy, yet its effectiveness boundaries have not been systematically explored.
- Divergent demands of long and short context tasks: Tasks with different context lengths may benefit differently from structured representations, requiring a fine-grained experimental analysis.
Method¶
AMR Construction and Linearization¶
- Using IBM's transition-based neural parsers (AMR3-structbart-L and doc-sen-conll-amr-seed42 models), text is parsed into document-level AMR structures.
- The AMRs are linearized into flat text representations and fed into LLMs.
Three Prompting Strategies¶
- Context-only (Baseline): Only the raw text context is provided.
- AMR-augmented: Both the raw text and its corresponding linearized AMR are provided to test whether AMR can facilitate context understanding.
- AMR-only: Only the linearized AMR is provided without the raw text to evaluate the LLM's capability to reason directly from AMRs.
Task Settings¶
Six task types are covered: context regeneration (AMR-to-text), single-hop question answering (SQuAD 2.0), multi-hop reasoning (HotpotQA), dialogue summarization (SAMSum), sentence-level NLI (SNLI), and document-level NLI (DocNLI). For each task, zero-shot, 3-shot, and 5-shot experiments are conducted.
Models¶
8-bit quantized, instruction-tuned models are used: Llama 3.1 (8B), Phi-3, and Mistral 7B. Additionally, rank-32 LoRA fine-tuning of Llama 3.1 is performed on the SAMSum dataset.
Key Experimental Results¶
AMR-to-text Regeneration Capability (LDC2020T02)¶
| Model | Shot | Cosine Similarity |
|---|---|---|
| Llama 3.1 | 0-shot | 73% |
| Llama 3.1 | 3-shot | 80% |
| Llama 3.1 | 5-shot | 81% |
| Phi-3 | 0-shot | 74% |
| Phi-3 | 5-shot | 76% |
| Mistral | 5-shot | 76% |
LLMs can effectively reconstruct raw text from linearized AMRs, with Llama 3.1 achieving 81% cosine similarity in the 5-shot setting.
SAMSum Dialogue Summarization (Llama 3.1 Cosine Similarity)¶
| Prompting Strategy | 0-shot | 3-shot | 5-shot |
|---|---|---|---|
| Context-only | 66% | ~74% | ~74% |
| AMR-augmented | 76% | ~75% | ~75% |
| AMR-only | ~60% | ~70% | ~68% |
- AMR augmentation yields a significant 10-percentage-point improvement in the zero-shot scenario.
- In few-shot settings, the advantage of AMR augmentation narrows but persists.
Short-Context Tasks (SQuAD 2.0, Llama 3.1 F1)¶
In contrast, AMR augmentation degrades performance in single-hop QA: 3-shot performance drops from 59% to 52%. AMR-only achieves a 48% F1 score in the 3-shot setting, but precipitously drops to 26% in the 5-shot setting, indicating that excessive AMR examples interfere with reasoning.
NLI Tasks¶
Phi-3 achieves the best performance on SNLI. AMR augmentation significantly improves macro F1 in the zero-shot setting (27% to 39%), while context-only remains superior in the few-shot setting (82%).
Highlights & Insights¶
- Systematic and comprehensive evaluation framework: A complete experimental matrix covering 6 tasks × 3 prompting strategies × 3 models × 3 shot settings.
- Actionable key findings: The clear and practical conclusion that AMR assists in long contexts but hinders in short contexts directly guides prompt design.
- LLMs indeed comprehend AMR: An 81% similarity in text reconstruction demonstrates that LLMs possess strong capabilities to interpret structured semantic representations.
- Extensibility to other structured representations: The methodological framework can be generalized to other structured formats, such as knowledge graphs or discourse representation structures.
Limitations & Future Work¶
- Lack of full parameter fine-tuning: Only LoRA fine-tuning was conducted (which underperformed compared to few-shot prompting), lacking a systematic comparison with full parameter fine-tuning.
- In-depth explanation of long-context advantages is missing: The fundamental reasons why AMR is beneficial in long contexts (e.g., info compression, preservation of key information) are not thoroughly analyzed.
- Limited model scale: The evaluated models are constrained to the 7-8B scale, with no exploration of larger models (e.g., 70B).
- No CoT prompting used in HotpotQA: This restricts the fairness and persuasiveness of the multi-hop reasoning experiments.
- DocNLI evaluated only on a subset of the test set: Verification on the full test set is required to draw reliable conclusions.
Related Work & Insights¶
- AMR-to-text generation: Zhu et al. (2019) leveraged graph structures to improve Transformer-based AMR generation quality; Koncel-Kedziorski et al. (2022) utilized graph Transformers for text generation from knowledge graphs.
- AMR-augmented NLP tasks: Hua et al. (2023) integrated AMR via text-graph attention to improve long dialogue summarization; Yang et al. (2024) incorporated AMR through a gating mechanism in dialogue evaluation.
- LLM prompting techniques: Chain-of-Thought prompting by Wei et al. (2023) and soft prompt tuning by Lester et al. (2021) point to future directions.
- Parameter-efficient fine-tuning: LoRA by Hu et al. (2021) and Adapters by Houlsby et al. (2019) present potential pathways for integrating structured representations with LLMs.
Rating¶
- ⭐⭐⭐ Novelty: The evaluation framework is systematic, but does not introduce new models or methods, leaning more towards empirical analysis.
- ⭐⭐⭐⭐ Practicality: The findings can be directly utilized to guide prompt design and structured information utilization strategies.
- ⭐⭐⭐⭐ Experimental Thoroughness: A comprehensive experimental matrix of 6 tasks × multiple variables, featuring complete confidence intervals.
- ⭐⭐⭐⭐ Writing Quality: Standardized structure, rich visualizations, and explicit methodological descriptions.