Structure-Enhanced LLM Generation

• Structure-enhanced LLM generation is a set of techniques that embed explicit structural cues (tables, graphs, mind maps) into LLM workflows to produce organized and interpretable outputs.
• It improves efficiency and factual accuracy by reducing human comprehension time and optimizing outputs for diverse tasks, including summarization, code synthesis, and molecule design.
• Methods such as divide-and-generate prompting, auto-critique, and structure-aware fine-tuning demonstrate measurable gains in SQL accuracy, chemical validity, and summarization quality.

Structure-enhanced LLM generation refers to a collection of techniques and methodologies aimed at augmenting LLMs with structural information—either in the input representation, within the generation workflow, or as explicit targets for output. The objective is to induce outputs that are not only semantically accurate but also organized, interpretable, and aligned with domain-specific structural constraints. Structure enhancement encompasses a broad array of modalities, including tables, graphs, mind maps, tree representations, context structurization, grammar-guided output, and structured external knowledge injection. Empirical results show substantial gains in accuracy, efficiency, interpretability, and time-to-comprehension across key tasks such as summarization, scientific information extraction, code and molecule generation, and downstream applications requiring structured prediction.

1. Structured Representation Modalities

A central feature of structure-enhanced approaches is the selection and encoding of suitable representation modalities. Common forms include:

• Tables: Partitioning complex information into rows and columns enables LLMs to produce highly organized, compact outputs and supports rapid assimilation of dense factual data. In controlled studies, structured table presentation yielded up to 42.9% reduction in human comprehension time relative to unstructured text, with accuracy maintained at 78% after auto-critique enhancements (Jain et al., 12 Jan 2024).
• Mind Maps and Hierarchical Structures: Mind maps offer a highly visual, flexible organization for sparse or conceptual content, leveraging a central “root” node with recursive branching. Iterative mind-mapping strategies, with local and global structure checks, led to a +37pp (absolute) improvement in factuality and coherence for mind map generation over basic LLM outputs (final accuracy 79%) (Jain et al., 12 Jan 2024).
• Graph and Tree-based Inputs: For domains where relationships are naturally graph-structured (e.g., molecules, AMRs, SQL queries), structure-aware encoding and integration is essential. For molecule generation, Graph-to-Tree (G2T-LLM) encoding yields a hierarchical JSON/XML representation, aligning with LLM pre-training corpora for tree-structured data. This bridges the gap between non-linear molecular graphs and model capabilities, producing near-100% chemical validity after fine-tuning (Yu et al., 3 Oct 2024).
• Context Structurization: Structured input formatting, such as hierarchical breakdowns into "Scope", "Aspect", and "Description," allows LLMs to process long-form and intricate textual contexts more effectively. This leads to significant improvements in reading comprehension and hallucination detection. For example, LLaMA2-70B with single-round structurization matched GPT-3.5-Turbo on exhaustive hallucination evaluation tasks (Liu et al., 23 Jul 2024).
• Discourse and Linguistic Structures: The inclusion of discourse structure (using frameworks like Rhetorical Structure Theory) in the reward and feedback loop of reinforcement learning aligns LLM output with human-like essay and report organization, consistently outperforming both standard and RLHF models on long-document generation tasks (Kim et al., 4 Apr 2025).

2. Prompting Strategies and Automatic Structure Critiques

The generation of high-quality, valid, structured outputs via LLMs is not trivial and requires substantial prompting engineering. Effective strategies include:

• Divide-and-Generate for Tables: Decomposition of passages into subtasks, with specific prompts, ensures that each semantic unit is mapped into the correct table cell, with tight control over column type adherence (Jain et al., 12 Jan 2024).
• Iterative Expansion for Mind Maps: Recursive prompting with "expand or terminate" decisions enables the LLM to control depth and breadth of hierarchical outputs and to repair local inconsistencies at each iteration (Jain et al., 12 Jan 2024).
• Two-Step Generate-and-Organize (G&O) Pipeline: Decoupling the extraction of semantic content from structured formatting (e.g., in information extraction) leads to substantial improvement: up to 15.8% F1 for partial match NER and 28.5% F1 for relation extraction compared to single-step prompting (Li et al., 20 Feb 2024).
• Auto-Critique and Taxonomy-Guided Verification: Automated critics operating at different levels—factuality (attributing claims to source spans), local structure (type-conformance), and global structure (format or abstraction uniformity)—systematically filter and repair outputs, yielding 79% and 78% absolute accuracy for mind maps and tables, respectively (Jain et al., 12 Jan 2024).
• Calibration and Confidence Estimation: For structured prediction, extracting well-calibrated confidence scores for candidate components through multiple white-box (token probability) and black-box (verbalization, sampling consistency) prompt strategies enables ILP-based combinatorial inference, further increasing output reliability (Pauk et al., 20 Aug 2025).

3. Structure-Aware Decoding, Backtracking, and Fine-Tuning

To guarantee output that satisfies both syntax and semantics, several advanced approaches have emerged:

• Grammar-Guided Iterative Generation and Backtracking: Libraries such as IterGen support structured decoding by mapping sequence tokens to context-free grammar symbols. This enables users to not only generate outputs in a controlled, symbol-oriented fashion (e.g., SQL segments, code blocks) but also to backtrack and repair outputs at the symbol level. Formalization includes functions C(·) for counting occurrences and symbol-position mapping via incremental LR parsing. Empirically, IterGen yields an 18.5% improvement in SQL test accuracy and complete elimination of privacy leakage in generated emails (Ugare et al., 9 Oct 2024).
• Structure-Aware Fine-Tuning: Approaches such as SAFT inject direction-sensitive graph positional encodings into input embeddings—without architectural modification—allowing LLMs to leverage graph structure (as for AMR-to-Text generation). The method achieves up to a 3.5 BLEU improvement over baselines on AMR 3.0, with gains scaling on structurally complex inputs (Kamel et al., 15 Jul 2025).
• Hybrid Prompting with Natural Language Structurization: When integrating structured data (e.g., AMRs, FOL, parse trees), mapping "code-like" structures to natural language descriptions (as in SR-LLM) before prompting or fine-tuning circumvents format-mismatch issues. In paraphrase detection, this led to +3.17%/12.38% F1 improvement for training-free and training-dependent settings, respectively (Zhang et al., 20 Feb 2025).
• Decomposition and Syntax-Based Prompting for SQL and Code: Decomposing natural language queries into meta-operations mapped from grammar trees, and encoding the database schema as a graph, allows for structure-guided, stepwise, and more accurate code (e.g., SQL) generation. On Spider, these approaches achieved 87.91% execution accuracy and 76.79% exact match, substantially beyond non-structure-aware alternatives (Zhang et al., 19 Feb 2024).

4. Applications, Evaluation, and Empirical Outcomes

Structure-enhanced LLM generation has demonstrated concrete value across applications:

• Factual and Efficient Summarization: Structured outputs reduce human comprehension time by 42.9% (tables) and 31.9% (mind maps), with no decrease in accuracy (Jain et al., 12 Jan 2024).
• Information Extraction: Generate-and-Organize improves both recall and precision in NER and RE across biomedical and scientific corpora, while supporting self-consistency and error aggregation (Li et al., 20 Feb 2024).
• Weak Supervision and Structure Discovery: Embedding-based structural refinement eliminates redundant and correlated labeling functions in Prompted Weak Supervision. The Structure Refining Module improved F1 by up to 12.7 points over basic PromptedWS on relation-extraction benchmarks, with controlled efficiency–performance trade-offs (Su et al., 2 Feb 2024).
• Recommendation and Retrieval-Augmented Generation: Dynamically retrieving structured subgraphs from a knowledge graph and tightly integrating graph neural encodings with LLM prompt embeddings (as in K-RagRec) reduces hallucinations and addresses cold-starts, improving both accuracy and efficiency compared with RAG or prompt-only methods (Wang et al., 4 Jan 2025).
• Scientific and Code Domains: Graph-to-tree and sequence-of-diversity methodologies in molecule design (Yu et al., 3 Oct 2024, Jang et al., 4 Oct 2024) produce diverse, valid candidates aligned with property requirements, while data translation (e.g., hdl2v) yields up to 23% improvement in Verilog generation pass@10 for large-code LLMs (Hong et al., 5 Jun 2025).
• Model-Based Engineering: Intermediate, format-independent conceptual instance models in JSON, compiled to target formal languages (e.g., XMI), achieve 100% syntactic validity across a range of models, dramatically simplifying the content–structure separation (Pan et al., 28 Mar 2025).

5. Limitations, Trade-Offs, and Future Research

Despite these advances, structure-enhanced LLM generation encounters several challenges:

• Input Format Compatibility: Representation formats for structured data (e.g., AMR code) that are not aligned with the model’s pretraining data can actually degrade downstream performance, necessitating transformation or naturalization strategies (Zhang et al., 20 Feb 2025).
• Scalability and Efficiency: Addition of full dependency structure discovery or symbolic ILP inference may impose substantial computational cost, especially as the number of structural variables increases (Su et al., 2 Feb 2024, Pauk et al., 20 Aug 2025).
• Overfitting and Redundancy: Excessive or imbalanced integration of structured information vs. natural language content risks redundancy or suboptimal performance in stronger LLMs—empirically, a 50-50 mix yields better results than pure-structured data fine-tuning (Zhang et al., 20 Feb 2025).
• Credit Assignment and Diverse Output: For sequence-of-structure tasks (e.g., molecular sequence generation), credit assignment for diversity must be handled at the object level rather than sequence level, requiring stage-wise RL and novel reward designs (Jang et al., 4 Oct 2024).
• Broader Applicability: While initial studies demonstrate value for graphs, trees, and tabular structures, further generalizations to multi-modal and more complex knowledge representations remain under-explored (Kamel et al., 15 Jul 2025).

Directions for future research include optimizing structure-to-language mappings, fusing structure-aware embeddings with internal attention mechanisms, expanding format-agnostic compilation, further developing batch and iterative grammar-guided generation frameworks, and refining calibration/fine-tuning strategies for robust structured prediction.

6. Summary Table: Core Methodologies and Empirical Gains

Method / Domain	Structural Enhancement Mechanism	Key Empirical Gain
Table/Mind Map Generation (Jain et al., 12 Jan 2024)	Prompt decomposition, iterative expansion, critics	+37pp mind map, +15pp table accuracy; –42.9% comprehension time
Weak Supervision (Su et al., 2 Feb 2024)	Embedding-based similarity, LaRe+CosGen	Up to +12.7 F1 (Spouse DS), improved efficiency
Molecule/Graph Generation (Yu et al., 3 Oct 2024)	Graph-to-tree encoding, fine-tuned constraints	98–99% valid molecules, competitive FCD/novelty
Diverse Molecule Generation (Jang et al., 4 Oct 2024)	Stagewise RL, autoregressive diversity conditioning	Higher NCircles/IntDiv, lower time cost vs. baselines
SQL Generation (Zhang et al., 19 Feb 2024)	Graph linking, syntax-based prompt decomposition	87.91% SQL exec accuracy (Spider), robust on hard queries
Information Extraction (Li et al., 20 Feb 2024)	Generate–Organize, explanation + structuring	+15.8%/28.5% F1 NER/RE vs. baseline
Discourse Alignment (Kim et al., 4 Apr 2025)	Dense structural RL reward, RST parsing	Human-like motif distribution, higher ROUGE and readability
Code Generation (Hong et al., 5 Jun 2025)	HDL-translation dataset, structure-augmented FT	+23% pass@10 VerilogEvalV2
Model Generation (Pan et al., 28 Mar 2025)	Format-independent conceptual model, compiler	100% XMI validity; 95–100% semantic precision (GPT-4o)

7. Theoretical and Practical Implications

Structure-enhanced LLM generation advances the field by incorporating explicit structural information at every stage—input formatting, model optimization, prompt and verification procedure, and output realization. This multi-dimensional approach increases the fidelity, interpretability, and efficiency of LLMs across a broad set of high-value domains, establishes new state-of-the-art results, and underpins promising directions for integrating structured, symbolic, or graph-based knowledge with generative neural models. The strategic use of structure in LLM workflows goes beyond mere formatting: it fundamentally expands the reasoning, generalization, and factual grounding capacities of LLMs.