LLM-based Preprocessing: Structured Data Insights

Updated 20 October 2025

LLM-based Preprocessing is a novel approach that leverages contextual LLM capabilities to transform raw, heterogeneous data into structured, high-quality representations.
The technique outperforms traditional pipelines by handling semantic ambiguities and noisy, domain-specific inputs with increased accuracy and robustness.
Key methodologies include semantic translation, dynamic data splitting, and feature extraction, enabling practical improvements in interpretability and downstream tasks.

LLM-based preprocessing refers to the use of LLMs to transform, clean, or structurally recast input data before it is consumed by downstream learning or reasoning modules. Unlike conventional preprocessing pipelines—which typically use rule-based, statistical, or heuristics-driven techniques—LLM-based preprocessing leverages the contextual, domain-specific, and generative capabilities of LLMs to convert raw, naturalistic, or heterogeneous data into structured, high-quality representations. This approach has been empirically shown to improve performance, robustness, and generalizability in diverse application areas, especially for tasks where traditional pipelines fall short due to semantic complexity, noise, or the need for interpretable outputs.

1. Theoretical Foundations and Key Motivations

The motivation for LLM-based preprocessing arises from three fundamental limitations of traditional pipelines:

Context-Agnostic Transformations: Conventional methods (e.g., static stopword lists, regex-based cleaning, rule-based lemmatizers) are generally context-free and cannot disambiguate cases where semantic meaning is determined by broader textual or task context (Braga et al., 13 Oct 2025).
Difficulty with Complex or Noisy Data: In scenarios involving ambiguous, domain-specific, or highly-heterogeneous data (e.g., scientific articles, legal documents, tables, engineering scripts), fixed pipelines lack the adaptability required to produce standardized, high-quality representations (Zhou et al., 2023, Tian et al., 14 Jul 2025, Zhang et al., 2023, Henriksson et al., 13 Jan 2025).
Bridging Human-Friendly and Machine Forms: LLMs can translate ambiguous natural language or domain-specific input directly into formal languages, structured representations, or informative features that are otherwise labor-intensive to produce (e.g., Planning Domain Definition Language (PDDL) (Zhou et al., 2023), Verilog metadata (Calzada et al., 9 Jul 2025)).

The incorporation of few-shot in-context learning, customized prompting, and instruction-tuning extends LLM-based preprocessing beyond language understanding into tasks involving code, tabular data, gene expression pipelines, or malware artifacts.

2. Principal Methodologies and Workflows

LLM-based preprocessing encompasses several core paradigms:

Paradigm	Example Application	Representative Details
Semantic Translation	Robotics planning	Natural language → PDDL via LLM translator (Zhou et al., 2023)
Dynamic Data Splitting & Renovation	Code script retrieval/generation	LLM segmenter creates semantically coherent chunks; IKEC/CoDRC/ATR for content renovation (Lin et al., 2023)
Feature Generation & Interpretable Vectors	Scientific text, Malware	LLM extracts high-level, domain-informed features (e.g., rigor, novelty, security indicators) (Balek et al., 11 Sep 2024, Marais et al., 13 Jun 2025)
Domain-specific Structure Extraction	Legal, Hardware, Tables	Legal/Lexical normalization, metadata extraction, schema cleaning, or code validation (Shu et al., 27 Jul 2024, Calzada et al., 9 Jul 2025, Tian et al., 14 Jul 2025)
Quality Filtering & Data Selection	Web text, Multilingual corpora	LLMs annotate or filter lines for quality; classifier models scale (Henriksson et al., 13 Jan 2025, Messmer et al., 14 Feb 2025)

Semantic Formalization

A hallmark is the translation of free-form language or domain data into structured "machine-consumable" forms. For example, ISR-LLM employs an LLM "translator" prompted with few-shot pairs to convert planning instructions into PDDL domain/problem files, defining predicates, types, preconditions, and effects. This pipeline provides a foundation for subsequent symbolic planning and formal validation (Zhou et al., 2023).

Semantic Data Splitting and Renovation

A semantic splitter LLM, as seen in engineering code applications, ensures input chunks are topically coherent—supporting more meaningful embeddings for retrieval and generation tasks within RAG architectures. Renovation steps (e.g., IKEC prompting) encourage LLMs to expand abridged descriptions, verified via techniques such as Chain of Density for Renovation Credibility (CoDRC) and Adaptive Text Renovation (ATR) (Lin et al., 2023). These mechanisms quantitatively relate added content and LLM-derived renovation confidence.

Interpretable Feature Generation

Rather than high-dimensional opaque embeddings, LLMs can be prompted to extract a compact set of semantically meaningful features (e.g., "methodological rigor," "novelty," binary indicators for discipline and research type) (Balek et al., 11 Sep 2024). Actionable rules are then distilled from these features, allowing not just classification but individual- or group-level intervention strategies.

Domain-Specific Preprocessing

LLM-based workflows generate structured prompts from raw legal case files, clinical/bioinformatics records, or hardware code repositories, reformatting them into regularized training examples or injection-ready representations for downstream LLMs, sometimes with rich metadata. For hardware code (Verilog), iterative syntactic/synthesizability checks, deduplication, and metadata extraction provide robust baselines for code generation/analysis (Calzada et al., 9 Jul 2025).

Quality filtering and multilingual data selection are handled by using LLMs to produce nuanced, fine-grained content labels, which are then operationalized at scale with efficient classifiers (e.g., DeBERTa-v3; FastText/MLP over XLM-R embeddings) (Henriksson et al., 13 Jan 2025, Messmer et al., 14 Feb 2025).

3. Empirical Outcomes and Quantitative Evaluation

LLM-based preprocessing yields empirically measurable improvements across a range of benchmarks and applications:

Task Planning Success Rates: In robotics long-horizon planning, ISR-LLM’s translation of natural language to PDDL significantly improved success rates of downstream self-refinement and validation modules over models that attempted direct planning from ambiguous input (Zhou et al., 2023).
Semantic Chunking and Enrichment: Segmenting technical documents by semantic topic, rather than arbitrary length, and then applying LLM-based renovation boosted the percentage of syntactically and logically correct code lines to 73.33% in engineering benchmarks (Lin et al., 2023).
Data Preprocessing (DP) Performance: Jellyfish-13B (LLM) matches or outperforms GPT-3.5/4 and non-LLM task-specific baselines on error detection, imputation, and entity/schema matching. Key metrics include F1 scores upwards of 90–100 for several DP tasks, along with competitive performance on unseen datasets (Zhang et al., 2023).
Explainable/Interpretable Outcomes: Interpretable feature extraction—generating only 62 compact features—achieved classification accuracy and SHAP-based explainability very close to state-of-the-art 768-dimensional SciBERT embeddings for citation and expert grading tasks (Balek et al., 11 Sep 2024).
Data Quality and Training Efficiency: LLM-labeled line-level filtering led to 0.1-point improvements in HellaSwag accuracy and up to 32% reduction in required training steps, indicating higher training efficiency with less data (Henriksson et al., 13 Jan 2025).
Domain-Specific Robustness and Security: Advanced two-tier preprocessing—including spelling correction and word splitting—significantly improved ML classifier robustness to LLM-generated/adversarial phishing, achieving detection accuracy up to 94.26% and F1-scores of 84.39% (Kulal et al., 13 Oct 2025). LLM-driven feature selection/transformation for IoT anomaly detection yielded macro-F1 jumps from 0.49 (PCA baseline) to 0.98 (Ghimire et al., 5 Mar 2025).

4. Challenges, Limitations, and Comparisons to Traditional Pipelines

While LLM-based preprocessing has clear advantages, certain challenges persist:

Ambiguity and Consistency: The stability of outputs with few-shot/in-context prompts depends on the nature and diversity of the provided examples and prompt context. Agent-based feedback for data conversion can be inconsistent, especially in complex domains such as genomics, where multiple agents may produce contradictory code conversion or trait extraction rules (Liu et al., 21 Jun 2024).

Computation and Scalability: Compared to fixed pipelines, LLM-based approaches have higher computational requirements for inference, especially in low-resource or high-throughput settings. For some tasks (e.g., stemming), LLMs lag behind specialized rule-based tools in accuracy/stability (Braga et al., 13 Oct 2025). However, improvements in runtime efficiency have been achieved through parallelization (e.g., line-level annotation with classifier scaling, TreeSitter consolidation for code (Henriksson et al., 13 Jan 2025, Gonçalves et al., 8 May 2025)).

Interpretability and Validation: In cases where LLM-generated features or representations are used for downstream rule learning (e.g., interpretable scientific features), their semantic alignment with human expert understanding must be validated through quantitative (e.g., chi-square significance, F1) and qualitative (e.g., action rule uplift) analyses (Balek et al., 11 Sep 2024).

Domain Adaptation and Tokenization: Domain-specific tokenization schemes (e.g., SentencePiece for legal LLMs) and tailored vocabulary construction are critical for reliable performance. Overlaps and variations in tokenization can lead to nontrivial effects on classification and attribution (Belew, 28 Jan 2025).

5. Application Domains and Extension Scenarios

LLM-based preprocessing has been successfully applied across:

Natural Language Planning and Robotics: Natural language instructions → PDDL conversion for symbolic planners and iterative self-refinement (Zhou et al., 2023).
Legal Document Analytics: Extraction of factual summaries, verdicts, and construction of structured training pairs from unstructured case files; adaptation to multi-task learning (case retrieval, precedent recommendation, verdict prediction) (Shu et al., 27 Jul 2024, Roodman, 14 Apr 2024).
Scientific and Biomedical Data: Feature engineering from text for interpretable prediction of research impact/expert assessment (Balek et al., 11 Sep 2024); agentic preprocessing of gene expression/clinical data, including mapping, deduplication, normalization, and merging for statistical genomics (Liu et al., 21 Jun 2024).
Tabular, Code, and Hardware Data: Schema cleaning and semantic normalization in real-world tables (Tian et al., 14 Jul 2025), advanced code generalization/tokenization for vulnerability and RTL code generation (with deduplication and synthesizability checks) (Gonçalves et al., 8 May 2025, Calzada et al., 9 Jul 2025).
Cybersecurity and Adversarial Robustness: Multi-stage text normalization, spelling correction, and segmentation in phishing detection (Kulal et al., 13 Oct 2025); automated semantic extraction from malware files for class interpretable scoring (Marais et al., 13 Jun 2025).
Data Quality for Pretraining: Line-level web text annotation and quality filtering (Henriksson et al., 13 Jan 2025); transformer/FastText-based model selection for multilingual corpora curation (Messmer et al., 14 Feb 2025).

6. Prospects, Best Practices, and Future Directions

Interoperability: Modular and layered LLM-based preprocessing frameworks (e.g., those separating structural normalization, domain adaptation, and semantic enrichment) enable composable, updatable pipelines suited to heterogeneous tasks (Tian et al., 14 Jul 2025).
Verification and Feedback: Integrated validation tools (e.g., PDDL validators, logic synthesis checks, statistical significance tests) should be part of benchmarks and application pipelines to guarantee correctness and interpretability at each preprocessing stage (Zhou et al., 2023, Calzada et al., 9 Jul 2025).
Prompt Engineering and Instruction Dataset Design: Careful curation of few-shot/instruction examples, using domain knowledge and targeted augmentation, enhances LLM reliability for standard and niche preprocessing tasks (Zhang et al., 2023, Lin et al., 2023).
Resource Sensitivity: LLM-based preprocessing is best justified where annotated resources are scarce or substantial gains in semantic quality or interpretability are required; it may be overkill for cases where lightweight rule-based pipelines suffice (Braga et al., 13 Oct 2025).
Open Data and Reproducibility: Public release of code, model checkpoints, prompts, and high-quality preprocessed data (e.g., Jellyfish DP datasets (Zhang et al., 2023), VerilogDB (Calzada et al., 9 Jul 2025), line-level web quality scores (Henriksson et al., 13 Jan 2025)) facilitates benchmarking and further research.

A plausible implication is that future advancements will further blur the boundaries between preprocessing, representation learning, and interpretable reasoning, with LLMs increasingly occupying the central role in data preparation, domain adaptation, and explainable machine learning pipelines.