PreprocessLM: LLM-Driven Data Preprocessing

Updated 15 January 2026

PreprocessLM is a set of prompt-driven LLM frameworks that standardizes data preprocessing using modular, chain-of-thought guided pipelines.
It enhances preprocessing tasks like error detection, imputation, normalization, and semantic standardization through fine-tuned prompt templates.
The framework is applied in analytics, education, cybersecurity, and more, consistently outperforming traditional rule-based methods.

PreprocessLM designates a family of prompt-driven, LLM–based frameworks for automating and enhancing data preprocessing across diverse computational domains. At its core, PreprocessLM operationalizes each preprocessing function—whether applied to tabular, textual, multimedia, or code data—as a sequence of LLM queries structured via targeted prompt templates. This approach enables advanced capabilities in error detection, imputation, normalization, augmentation, and semantic standardization, frequently outperforming classical rule-based or statistical paradigms. The architecture’s extensibility has fostered adoption in analytics, education, engineering, cybersecurity, and adversarial content analysis, underlining its emergence as a canonical methodology for “model-centric” preprocessing (Zhang et al., 2023, Meguellati et al., 24 Feb 2025, Nijdam et al., 8 Jan 2026, Lin et al., 2023, Marais et al., 13 Jun 2025, Meguellati et al., 22 Apr 2025).

1. Core Architectural Principles and Workflow

PreprocessLM systems are structured around four principal modules:

Contextualizer: Transforms heterogeneous raw input (tabular records, document segments, curriculum descriptions, code, or multimedia annotations) into unified text strings. Missing values or metadata are explicitly encoded.
Prompt Constructor: Assembles zero-shot or few-shot instructions and illustrative exemplars; enforces explicit output formats and chain-of-thought directives to standardize LLM completion for downstream parsing.
LLM Inference: Dispatches batched or single-instance prompts to black-box LLMs (e.g., GPT-4(o), LLaMA 3.1, Qwen-2.5b-7B-Instruct), optionally invoking chain-of-thought or domain-anchored reasoning. Batch prompting reduces inference cost and latency.
Post-Processor: Extracts labels, imputed values, explanations, entity matches, or semantic chunks from LLM output; reconverts response to the designated machine-friendly representation.

Canonical tasks enabled by PreprocessLM include error detection (“Does $r_j$ contain an error?”), imputation (“Infer missing value $r_j$ ”), schema/attribute matching, entity resolution, and context-enriched augmentation (Zhang et al., 2023).

2. Prompt Engineering and Adaptative Strategies

PreprocessLM relies on fine-grained prompt engineering, drawing from chain-of-thought protocols and few-shot in-context learning (Zhang et al., 2023, Lin et al., 2023). Prompts specify the expert role (database engineer, curriculum designer, code generator), the precise instance for processing, the answer format (step-by-step reasoning followed by final output), and batch instructions when multiple items are supplied. For curriculum extraction, free-form descriptions are distilled into bullet lists of subtopic phrases (Nijdam et al., 8 Jan 2026), while in code generation, dynamic chunking relies on prompts that request semantic-preserving splits rather than naive windows (Lin et al., 2023).

Template examples:

Data Imputation:

You are a database engineer.
Please infer the missing "city" attribute.
Think step by step before answering.
Format:
  Reason: <your reasoning>
  Answer: <the city name>
Instance:
  name: "Carey’s Corner", addr: "1215 Powers Ferry Rd.", phone: "770-933-0909", hoursperweek: missing

Cleaning and Repair (Text/Image):

You are an AI assistant that cleans and corrects image descriptions.
Improve the following description by fixing grammatical errors, removing repetitive phrases, and ensuring it is clear and coherent.
Provide only the cleaned description without any additional notes or explanations.
If the description is too corrupted to fix, respond with ‘INVALID DESCRIPTION’.

Batched prompting, clustering, and adaptive template selection are used for cost/time optimization and domain adaptation (Zhang et al., 2023, Meguellati et al., 24 Feb 2025).

3. Mathematical Formalization and Evaluation Metrics

PreprocessLM tasks are generally evaluated in inference-only mode using standard supervised metrics:

Accuracy: $\mathrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$
F1 Score: $F_1 = 2 \cdot \frac{\mathrm{Precision} \cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}$ , with $\mathrm{Precision} = \frac{TP}{TP + FP}$ , $\mathrm{Recall} = \frac{TP}{TP + FN}$
Hierarchical F1 (h-F1): For multi-label hierarchical tasks, e.g., persuasion detection in memes, hierarchical F1 is utilized (Meguellati et al., 24 Feb 2025, Meguellati et al., 22 Apr 2025).

For code renovation, quantitative metrics such as Percentage of Correct Lines (PCL) are introduced:

$\mathrm{PCL} = \frac{\text{Number of error-free lines}}{\text{Total number of lines}} \times 100\%$

Renovation credibility is assessed by growth ratios, standardized confidence deviations, and a tunable threshold $\kappa$ (Lin et al., 2023).

4. Domain Applications and Case Studies

PreprocessLM has been validated across multiple domains:

Tabular Data Analytics: Tasks of error detection, imputation, schema matching, and entity resolution on public datasets (Adult, Hospital, Amazon-Google, etc.) yield F1/accuracy up to 100%, outperforming classical baselines (Zhang et al., 2023).
Multimodal and Social Media Analysis: Caption cleaning for BLIP/GIT outputs, context-rich explanations, and trigger annotation for harmful content detection; LLMs yield modest to significant gains in F1, with GPT-4 cleaning statistically outperforming counterparts (Meguellati et al., 24 Feb 2025, Meguellati et al., 22 Apr 2025).
Curriculum Extraction: Standardizes narrative curriculum content into short “topic units” for robust downstream classification of Knowledge Areas, boosting macro-F1 and inter-rater agreement with experts (Nijdam et al., 8 Jan 2026).
Code Generation: Dynamic chunking, renovation, and enrichment for engineering scripts; integration with RAG pipelines and use of IKEC prompts provide up to 73.33% PCL in real-world code generation (Lin et al., 2023).
Malware Analysis: Semantic preprocessing constructs expert-readable JSON representations from both static and behavioral PE features; subsequent BERT classifiers achieve weighted-average F1 of 0.94, improving interpretability and explainability (Marais et al., 13 Jun 2025).

Domain	Input Type	PreprocessLM Output
Tabular Analytics	Table records	Unified text strings, labels
Multimedia	Captions (BLIP/GIT)	Cleaned text, explanations
Curriculum Design	Paragraphs, KDs	Subtopic lists
Code Generation	Scripts, docs	Dynamic chunks, enriched versions
Malware Analysis	PE binaries	JSON semantic reports

5. Computational Considerations and Limitations

Token usage and runtime scale directly with input complexity and batch size. Without batching, LLM calls incur significant costs (Adult ED, $4.8$ h and $\$8.14 $for GPT-3.5; batching at 15 reduces to$ 1.6 $h and$ \$3.00$) (Zhang et al., 2023). Latency constraints and throughput bottlenecks restrict practical parallelism, with open-source models such as Vicuna-13B frequently timing out. Scalability issues arise for datasets with millions of rows, nontrivial prompt construction/parsing, and domain-specific jargon ambiguities (Zhang et al., 2023, Meguellati et al., 24 Feb 2025). Caption cleaning LLMs vary in stringency—LLaMA 3.1 retains nearly all captions, Sonnet 3.5/GPT-4 discard more aggressively (Meguellati et al., 24 Feb 2025). In code renovation, confidence+growth thresholds tune precision at the expense of coverage (Lin et al., 2023).

6. Extensions, Improvements, and Future Directions

Continuous research explores hybrid pipelines fusing LLM-generated judgments with domain-tuned neural adapters, retrieval-augmented prompting for enriched contextual knowledge, adaptive batching for cost-efficiency, and fine-grained prompt optimization strategies (Zhang et al., 2023, Meguellati et al., 24 Feb 2025, Meguellati et al., 22 Apr 2025, Lin et al., 2023). The interface and impact of PreprocessLM have expanded beyond text normalization to semantic augmentation, knowledge distillation, and explainability, with applications in cybersecurity role-alignment (Nijdam et al., 8 Jan 2026) and analyst-driven malware categorization (Marais et al., 13 Jun 2025). Ongoing directions include systematic ablation studies of PreprocessLM components (e.g., extraction vs. augmentation), cross-domain retraining, and interpretability analysis grounded in transparent, expert-anchored semantic features.

7. Practical Reproducibility Guidelines

Empirical reproducibility of PreprocessLM pipelines is ensured by:

Assembling task-specific prompt templates (zero-shot/few-shot, explicit answer formats, chain-of-thought instructions)
Contextualizing raw inputs and applying batch-based prompting strategies
Parsing LLM-produced responses into machine-usable formats
Evaluating via accuracy and F1 formulas, possibly hierarchical metrics for multi-label or structural tasks
Integrating domain-specific postprocessing for interpretability (e.g., structured JSON in malware analysis (Marais et al., 13 Jun 2025), explanation/trigger lists in content moderation (Meguellati et al., 22 Apr 2025))
Adjusting hyperparameters such as batch size, temperature, explanation multiplicity, and confidence thresholds for optimal trade-offs

The PreprocessLM paradigm offers a replicable, modular blueprint for researchers seeking to harness LLMs for advanced, context-aware data preprocessing across computational disciplines (Zhang et al., 2023, Nijdam et al., 8 Jan 2026, Lin et al., 2023, Marais et al., 13 Jun 2025, Meguellati et al., 22 Apr 2025).