LLM-Assisted Data Cleansing Method

Updated 5 September 2025

LLM-assisted data cleansing is a method that combines traditional data quality principles with semantic inference to detect, correct, and standardize datasets.
It leverages context-aware parsing and correction to automatically address errors, enhance records, and consolidate data into reliable golden records.
Empirical results indicate substantial efficiency improvements, including over 50% reduction in manual effort and F1-scores exceeding 90% in data quality benchmarks.

An LLM-assisted data cleansing method refers to workflows and frameworks in which LLMs are incorporated—either centrally or as components—to detect, correct, explain, and/or standardize errors in structured, semi-structured, or unstructured datasets. These methods expand the scope of traditional, rule-based, and statistical cleansing by leveraging LLMs’ contextual reasoning and natural language understanding for intelligent, context-aware, and interactive data quality improvement. The sections below summarize the contemporary principles, operational mechanisms, and emerging paradigms established in recent research.

1. Core Principles and Workflow Design

LLM-assisted data cleansing builds upon classical data quality principles—completeness, correctness, consistency, and timeliness—while introducing semantic inference, explainability, and automation. The canonical workflow, as established in research information systems and extended to other domains, comprises:

Parsing: Decomposing raw input into tokens based on metadata. LLMs enhance traditional tokenization by interpreting context and identifying implicit structure, including non-standard abbreviations and variants.
Correction and Standardization: Detecting and fixing inconsistencies or errors, then formatting data to a consistent schema. LLMs can propose corrections informed by domain knowledge and context.
Enhancement: Enriching records with supplementary data (e.g., filling gaps using external sources or inferred relationships). LLMs contribute by extracting relevant details from unstructured fields or offering inferred values.
Matching: De-duplicating and linking entities across diverse sources. LLMs can disambiguate semantically similar but lexically distinct values (like “Lena Scott” and “Scott, Lena”) via advanced string similarity or entity resolution.
Consolidation: Merging redundant or partially overlapping records into comprehensive "golden records," with conflicts resolved through precedence rules or inference.

A typical algorithmic summary is:

for r in record_set:
    tokens = parse_data(r)                         # LLM-enhanced or rule-based parsing
    standardized = correct_and_standardize(tokens) # Standardization with possible LLM prompts
    enhanced = enhance_data(standardized)          # LLM-driven augmentation if gaps detected

matching_groups = match_records(record_set)
final_records = consolidate(matching_groups)
return final_records

LLM involvement is typically via direct API calls, in-prompt chain-of-thought reasoning for decision support, or autonomous multi-agent workflows as in CleanAgent (Qi et al., 13 Mar 2024).

2. Semantically-Aware Error Detection and Correction

Conventional statistical or deterministic rules can miss subtle or context-dependent errors. LLMs supply semantic understanding by (a) interpreting the intended meaning of attribute values, (b) recognizing outliers not just statistically but in context, and (c) generating repair rules as natural language explanations or executable code.

For example, in data cleaning systems such as Cocoon (Zhang et al., 21 Oct 2024), the LLM is provided with profiled value samples and metadata, and produces:

String normalization rules (e.g., “CASE WHEN column = 'English' THEN 'eng' ...”)
Regex pattern detections (e.g., recognizing dates in variant formats)
Human-verifiable comments explaining "why" a rule is suggested

This interpretability is particularly critical in research domains or regulatory settings, supporting both error tracking and explainable modifications.

3. Intelligent Standardization, Matching, and Consolidation

LLMs dramatically improve standardization by inferring format mappings and correcting typographical, structural, or semantic inconsistencies. For example, Dataprep.Clean with LLM-based agent orchestration (Qi et al., 13 Mar 2024) allows column-wise standardization (dates, addresses, phone numbers) with minimal code, automatically generating and iteratively refining transformation logic based on feedback.

Advanced matching leverages LLMs’ entity understanding and embedding-based similarity (e.g., BERT embeddings used in DataAssist (Goyle et al., 2023)) to resolve duplicates across partially conflicting records. Consolidation—building unified "golden records"—draws on both LLM-suggested merges (based on multi-criteria context) and automated rule composition, as in the parsing-to-consolidation pipeline for building RIS datasets (Azeroual et al., 2019).

4. LLM-Guided Workflow Automation and Orchestration

AutoDCWorkflow (Li et al., 9 Dec 2024) exemplifies end-to-end LLM-driven automation, where the model reasons about data cleaning steps as follows:

Column Targeting: LLM identifies columns relevant to the analytic "purpose," reducing unnecessary processing.
Quality Assessment: The model inspects data (possibly via summarized samples) and issues a Data Quality Report covering accuracy, relevance, completeness, and conciseness.
Operation Generation: LLM selects and configures cleaning operations (e.g., upper, trim, regex transforms) with arguments, forming a reproducible operation sequence ("workflow").

Empirical evaluation demonstrates that such agents not only match but can exceed the efficiency and breadth of hand-crafted pipelines, especially when paired with purpose-driven or benchmark-based evaluation frameworks.

5. Explainability, User Interaction, and Human-in-the-Loop Verification

LLMs contribute novel forms of explainability: all cleaning decisions—especially those that would otherwise be opaque (e.g., in MLN-based or probabilistic models)—can be explained in fluent language. For human-in-the-loop settings, systems can present:

Chain-of-thought justifications for each correction or merge (e.g., “Merged because ORCID numbers match and names are semantically equivalent”)
Transparent rationale tied to field enrichment, record linkage, or outlier removal
Natural language queries to clarify ambiguous mappings or prompt corrections, as seen in interactive harmonization tools (Santos et al., 10 Feb 2025)

Such methods increase user trust and make model-in-the-loop workflows feasible for highly sensitive domains.

6. Benefits, Limitations, and Challenges

Reported Benefits

Dimension	LLM-Assisted Methods	Conventional Methods
Error detection	Contextual, semantic, cross-field, and fuzzy matching	Lexical, syntax-based, or rule-anchored
Correction precision	Context-aware, can generate explanations	Rigid, less robust to variation
Workflow automation	End-to-end, adaptive, low/no-code interfaces	Manual or sequential, high technical burden
Efficiency	Dramatic reduction in time and manual effort	Labor-intensive, error-prone

Challenges

Computational Cost: LLM inference and resource requirements remain higher than statically coded flows or deterministic extractors (Ma et al., 2023).
Output Hallucination: Unsupervised LLM suggestions can produce plausible but incorrect formatting or content; mitigation requires explicit output constraints and possibly ensemble or retrieval-augmented approaches (Zhou et al., 4 Feb 2024, Menon et al., 5 May 2025).
Determinism and Testing: Reproducibly consistent outputs for unit testing lag behind deterministic UDF-style methods.
Data Privacy and Security: Sending raw records or metadata to LLM APIs may conflict with privacy policies, necessitating "intermediate rule" approaches or data minimization as in DeLTa (Ye et al., 23 May 2025).
Handling Large or Complex Datasets: Context window constraints and input length limits make sample-based or batched cleansing necessary for very large datasets (Zhang et al., 21 Oct 2024, Ma et al., 2023).

7. Empirical Impact and Future Research Directions

Across multiple domains, LLM-assisted data cleansing frameworks have demonstrated:

Substantial reduction in labor and cost—for example, 100x lower cost over manual digitization in panel data extraction (Bäcker-Peral et al., 16 May 2025), or over 50% reduction in manual time in business and forecasting applications (Goyle et al., 2023).
Higher F1-scores and cleansing accuracy (often exceeding 90%) on real-world and synthetic benchmarks compared with state-of-the-art holistic or probabilistic models (Gao et al., 2019, Zhang et al., 21 Oct 2024, Biester et al., 29 Apr 2024).
Enhanced trust and auditability, facilitating robust decision making in research, business, ESG reporting, and regulatory compliance (Menon et al., 5 May 2025).

Challenges in explainability, scaling, integration with formal rule-based or probabilistic engines, and robust interactive interfaces—especially for continual data change—remain active topics for further research (Zhou et al., 4 Feb 2024, Santos et al., 10 Feb 2025, Li et al., 9 Dec 2024).

In summary, LLM-assisted data cleansing methods represent an integration of semantic, context-aware AI reasoning into structured data quality workflows. Combined with automation, explainability, and interactive human control, these methods surpass many limitations of rule-based or statistical-only approaches, but demand continued research in determinism, validation, and efficiency for broad, high-stakes deployment.