LLMs Structural Extraction

• LLM-based structural extraction is a process that transforms raw, unstructured data into formal, machine-usable formats using techniques like schema induction and knowledge triple extraction.
• Modular pipelines employing data ingestion, prompt engineering, and multi-agent architectures enable precise extraction of parameters, entities, and geometric details.
• Experimental evaluations demonstrate improved accuracy and efficiency in applications such as finite element modeling, knowledge graph construction, and multimodal analysis.

LLM-based structural extraction encompasses a suite of automated methodologies whereby LLMs are configured to identify, formalize, and operationalize latent structural information from unstructured or semi-structured input sources. The goal is to transform text, code, tabular data, images, or multimodal artifacts into structured representations supporting downstream computational tasks. Structural extraction by LLMs is now foundational in fields such as computational engineering, scientific information management, web-based knowledge graph construction, and automated program synthesis, owing to rapidly improving prompt engineering, in-context learning, and task-adapted system design (Liang et al., 13 Apr 2025, Geng et al., 6 Oct 2025, Li et al., 10 Dec 2025).

1. Problem Formulations and Structural Targets

LLM-driven structural extraction formalizes input–output mappings from raw data to abstract, machine-usable structures. The “structure" may denote:

• Parametric representations (e.g., geometry, material data, and topology required for finite element models (Liang et al., 13 Apr 2025))
• Knowledge triples (subject–predicate–object units obtained from web documents for knowledge graphs and question answering (Sun et al., 29 Sep 2025))
• Argument-role and event structures (instantiated procedural steps or experimental parameters in scientific texts (Rathore et al., 17 Dec 2025))
• Schema induction (discovery of entities, attributes, and relationships to define canonical ontologies (Sadruddin et al., 1 Apr 2025))
• Vectorized topological artifacts (ordered coordinate sequences such as building contours in images (Zhang et al., 7 Jul 2025))

Letting $X$ denote the input data and $Y$ the formalized structure, the central modeling object is generally $p_\theta(Y \mid X, I)$, where $I$ encodes task- or schema-specific instructions. Structural extraction tasks thus become conditional generation, classification, or slot-filling problems parameterized by prompt and context.

2. Architectural and Prompting Frameworks

State-of-the-art LLM-based pipelines separate structural extraction into modular, explicitly engineered layers to increase reliability, transparency, and extensibility:

• Layered pipeline decomposition: Data ingestion, model inference (parameter extraction, model generation, verification), and structured output synthesis (Liang et al., 13 Apr 2025, Yoo et al., 28 May 2025, Li et al., 10 Dec 2025).
• Domain-specific prompt design: Integration of in-context exemplars, rules enforcing geometric or semantic invariants, and commonsense reasoning templates to guide the model's structural reasoning (Liang et al., 13 Apr 2025).
• Instruction-guided structuralization: General frameworks employ a concatenation of “prefix” (specifying extraction intent), raw content, and “suffix” (formalization directive) to steer output formats (Ni et al., 2023).
• Multi-agent architectures: Complex analyses are decomposed into subtasks, each managed by a specialized agent (e.g., geometry parsing, boundary assignment, code synthesis) to ensure determinism and incremental validation (Geng et al., 6 Oct 2025).
• Multi-step and hierarchical workflows: Iterative extraction (segmentation, filtering, confirmation) and self-refinement loops (candidate generation, boundary correction, pairwise selection) are critical for increasing extraction precision and recall (Yoo et al., 28 May 2025, Zhang et al., 2024).

Table: Canonical Layers in LLM-Based Structural Extraction Pipelines

Layer/Module	Essential Function	Reference
Data Layer	Raw input ingestion, system instructions	(Liang et al., 13 Apr 2025)
Extraction/Analysis	Parameter/entity/triple/event extraction	(Liang et al., 13 Apr 2025, Sun et al., 29 Sep 2025, Rathore et al., 17 Dec 2025)
Transformation	Structured code or schema generation	(Liang et al., 13 Apr 2025, Sadruddin et al., 1 Apr 2025)
Verification	Consistency or alignment checks (often LLM + algorithmic)	(Geng et al., 6 Oct 2025, Khamsepour et al., 3 Sep 2025)
Output/Reporting	Synthesis into human- or machine-readable reports/results	(Liang et al., 13 Apr 2025, Li et al., 10 Dec 2025)

3. Formalism, Mathematical Modeling, and Schema Dynamics

Structural extraction tasks are characterized by well-defined mathematical formulations:

• Finite element modeling: Structural parameters (nodes, connectivity, material properties) are translated into code whose correctness is verified by compliance with FE assembly and equilibrium equations (e.g., $\mathbf{K}\mathbf{u} = \mathbf{f}$ with $\mathbf{K}_e$ stiffness expressions) (Liang et al., 13 Apr 2025).
• Knowledge triple extraction: Extraction loss $$\mathcal{L}_{\text{extraction}}(\phi) = -\sum_i \log p_\phi(t_i \mid X)$$, with $t_i = (s_i, r_i, o_i)$, and joint training with task losses as $$\mathcal{L}_{\text{total}} = \mathcal{L}_{QA}(\theta) + \lambda \mathcal{L}_{\text{extraction}}(\phi)$$ (Sun et al., 29 Sep 2025).
• Program synthesis and model generation: LLMs generate code through emission rules parameterized by extracted JSON-like structures, often informed by domain constraints and invariants (Liang et al., 13 Apr 2025, Geng et al., 6 Oct 2025).
• Schema adaptation: Modular frameworks (e.g., SciEx) support dynamic changes to extraction targets via explicit schema templates, enabling prompt-only adaptation for evolving scientific data needs (Li et al., 10 Dec 2025).
• Event-based extraction: Span-level extraction and argument assignment are implemented via calibrated and stepwise LLM queries, enhanced by secondary models for span boundary correction (Zhang et al., 2024, Rathore et al., 17 Dec 2025).

4. Experimental Results, Metrics, and Error Analysis

Comprehensive evaluation of LLM-based structural extraction systems reveals task-dependent performance and common failure modes:

• Finite element pipeline accuracy: The GPT-4o-based pipeline achieved 100% benchmark accuracy, significantly surpassing GPT-4 (85%), Gemini 1.5 Pro (80%), and Llama-3.3 (30%), with domain-specific prompt additions yielding up to 30% improvement on asymmetric problems (Liang et al., 13 Apr 2025).
• Knowledge extraction for QA: Triple augmentation and multi-task learning produced substantial QA gains (+12–13 points accuracy in small LLMs); however, triple extraction F1 dropped sharply in web-scale, noisy settings (raw HTML: F1≈13–14%) (Sun et al., 29 Sep 2025).
• Hierarchical event and argument extraction: ULTRA boosted EM F1 from 25.2% to 39.4% recall and up to 32.7% overall, outperforming supervised and ChatGPT baselines (Zhang et al., 2024); ZSEE analysis showed event-type F1 in the 80–90% range, but argument span extraction plateaued at 57–66% F1 (Rathore et al., 17 Dec 2025).
• Schema extraction and semantic grounding: Human-in-the-loop schema mining workflows achieved high semantic alignment (BERTScore ≈ 0.8) across stages and LLMs, with GPT-4o outputs aligning most closely with expert references (Sadruddin et al., 1 Apr 2025).
• Engineering documentation extraction: Multi-agent LLM systems for 2D frame analysis achieved >80% end-to-end code correctness in most benchmarks, especially with deterministic rule-based geometry agents (Geng et al., 6 Oct 2025); hybrid LLM–algorithmic critique-refine loops in diagram synthesis achieved correctness up to 86% and completeness up to 89% (Khamsepour et al., 3 Sep 2025).

Error sources include layout mistakes (missing or misplaced components), failure to enforce sign or counting conventions, span-boundary imprecision, and hallucination of structure not present in the source. Failures are mitigated through prompt engineering (directional/number reasoning), negative sampling, verification loops, and human-in-the-loop schema refinement.

5. Domain Customization and Cross-Domain Insights

LLM-based structural extraction frameworks are extensible across domain boundaries by:

• Prompt adaptation: Structured prompts generalized with task- or domain-specific prefixes, suffixes, or embedded dictionaries enable deployment on new scientific, financial, or social science document types (Ni et al., 2023, Yoo et al., 28 May 2025, Aggarwal et al., 3 Nov 2025).
• Segmentation-then-extraction for long, complex documents: Document segmentation (by methods, table, or section) followed by candidate identification and relation extraction is essential for scaling to long-context settings (Yoo et al., 28 May 2025, Li et al., 10 Dec 2025, Aggarwal et al., 3 Nov 2025).
• Ontology integration: Automated schema mining can be grounded in external ontologies via LLM-driven candidate ranking and embedding similarity, yielding semantically coherent knowledge graphs (Sadruddin et al., 1 Apr 2025).
• Multimodal structural extraction: Vision-language LLMs enable direct pointwise regression of structured objects (building contours, tables) from images, outperforming classic pixel segmentation–vectorization pipelines (Zhang et al., 7 Jul 2025).

Table: Exemplary Structural Extraction Domains and Methods

Domain	Structural Target	Extraction Method	Principal Reference
Structural Analysis	FE models, input scripts	API-driven prompt cascade + code synthesis	(Liang et al., 13 Apr 2025, Geng et al., 6 Oct 2025)
SAT Optimization	Encoding structure, heuristics	Code analysis, variable clustering	(Schidler et al., 24 Jan 2025)
QA/KG Construction	Knowledge triples	Triple extraction, joint-training	(Sun et al., 29 Sep 2025)
Scientific IE	Schema, arguments, events	Segmentation + multi-step prompt	(Yoo et al., 28 May 2025, Rathore et al., 17 Dec 2025)
Schema Discovery	Entity–relation schema	LLM–human-in-the-loop workflow	(Sadruddin et al., 1 Apr 2025)
Vision	Polygonal/contour structure	VLM + LLM coordinate regression	(Zhang et al., 7 Jul 2025)

6. Verification, Stability, and Limitations

Verification and stability challenges remain central:

• Grounded extraction and hallucination reduction: SafePassage applies local alignment and NLI-based entailment to ensure outputs are textually grounded, achieving up to 85% reduction in hallucinations with precision up to 92.8% (Barrow et al., 30 Sep 2025).
• Determinism and generative stability: Best-of-n inference, prompt compression, and post-hoc model validation are necessary due to LLM stochasticity (generative stability for asymmetric structures ranging from 40–100%) (Liang et al., 13 Apr 2025).
• Hybrid algorithmic–LLM refinement: Algorithmic critiques eliminate structural violations systematically missed by LLMs, boosting semantic correctness/completeness by up to 17.8/13.2 percentage points, with modest LLM call overhead (Khamsepour et al., 3 Sep 2025).
• Low-resource adaptation: Zero- and few-shot instruction tuning enables domain transfer, but high precision or rare-entity extraction still demands expert feedback, corpus expansion, or active learning (Yoo et al., 28 May 2025, Zhang et al., 2024, Sadruddin et al., 1 Apr 2025).

7. Outlook: Scalability, Generalization, and Future Research

LLM-based structural extraction is characterized by rapid cross-domain scalability and task flexibility, but faces persistent obstacles in robustness, fine-grained boundary control, and hallucination resistance. Promising directions include:

• Hybrid symbolic-neural designs: Algorithmically enforced constraints and LLM-driven reasoning are increasingly combined for verifiable output (Khamsepour et al., 3 Sep 2025).
• Rich schema/ontology integration: Automated mapping to and discovery of scientific ontologies will further semantic interoperability (Sadruddin et al., 1 Apr 2025).
• Advanced multi-agent decomposition: Specialized agent cascades provide modular error localization and deterministic inferencing (Geng et al., 6 Oct 2025).
• Improved verification layers: Enhanced multimodal and multistep verification strategies are needed to address residual instability and misalignment, especially for visually or tabularly complex inputs (Li et al., 10 Dec 2025).
• Scaling to real-world, noisy data: Benchmarks on semi-structured, noisy, or long-context data show substantial accuracy drops, indicating a need for tailored cleaning, segmentation, and error recovery workflows (Sun et al., 29 Sep 2025, Aggarwal et al., 3 Nov 2025).

LLM-based structural extraction thus encompasses principled, modular, and highly customizable pipelines of prompt-driven neural inference, structured reasoning, and verification, now extensively validated across engineering, scientific, and digital knowledge domains. Ongoing developments are expected to further close the remaining gap to human-level fidelity on challenging extraction tasks.