LLM-Based Annotation Pipeline

• LLM-based annotation pipelines are automated frameworks that use large language models, prompt engineering, and expert validation to build annotated datasets.
• They integrate sequential steps such as data preprocessing, tailored prompt design, LLM inference, and iterative human review to ensure schema fidelity.
• Empirical results show performance improvements, with metrics like LAS increasing from 76.32% (LLM-only) to 95.29% after expert corrections.

LLM-based annotation pipelines are automated or semi-automated frameworks that integrate LLMs into the construction, refinement, and evaluation of annotated linguistic and structured datasets. These systems increasingly underpin modern approaches in NLP, multimodal learning, information extraction, and resource creation for low-resource and code-switched settings. They exploit LLMs’ capabilities in zero- and few-shot generalization, flexible schema adherence, and rapid processing, while addressing or circumventing weaknesses through expert review, post hoc validation, and structured prompt engineering.

1. Architectures and Core Workflows

LLM-based annotation pipelines share several foundational components: data ingestion, prompt-driven model inference, post-processing, and human verification or augmentation. Frameworks such as BiLingua Parser for Universal Dependencies (UD) in code-switched data (Kellert et al., 8 Jun 2025), MEGAnno+ for collaborative general NLP tasks (Kim et al., 28 Feb 2024), and chain ensembles for scalable multi-model annotation (Farr et al., 16 Oct 2024) exemplify architectural diversity. The typical architecture is as follows:

• Preprocessing: Tokenization, segmentation, language-ID tagging when relevant (essential for code-switching) (Kellert et al., 8 Jun 2025).
• Prompt Design: Task-specific prompt construction, usually employing few-shot exemplars for schema fidelity and robust language-pair handling (Kellert et al., 8 Jun 2025).
• LLM Inference: Sequential or batch annotation via model APIs, commonly with temperature set to zero for deterministic output and prompt length constrained by model capacity.
• Expert Review or Self-Correction: Manual review using specialized annotation tools, enforcing guidelines and resolving ambiguities (e.g., expert revision with inter-annotator κ = 0.85 (Kellert et al., 8 Jun 2025)).
• Iterative Refinement: Integration of expert-generated corrections into prompt updates, or further in-context demonstrations (“dynamic few-shot”).
• Export and Release: Output in standard formats (e.g., CoNLL-U, JSON, task-defined schemas).

A schematic of the BiLingua Parser instantiates these modules for UD-based parsing of mixed-language utterances, integrating LLM-generated CoNLL tables, specialized handling of code-switching phenomena, and expert post-processing.

2. Prompt Engineering and Language-Specific Heuristics

Prompt engineering is critical to scaffold LLM outputs toward valid annotation schemas, especially in structurally complex or resource-scarce scenarios.

• Task-Adaptive Prompt Design: For code-switched UD parsing, prompts include (i) a UD reference sheet, (ii) explicit instruction to annotate in columnar format, and (iii) 2–3 few-shot CoNLL-style examples highlighting edge phenomena (e.g., “Split contractions,” “Handle repeated tokens as subjects with shared heads,” “Use orphan for elliptical constructions”) (Kellert et al., 8 Jun 2025).
• Constraint Enforcement: The prompt is structured to enforce a single root per sentence and prohibit out-of-scope label induction; it preserves original tokenization, making exceptions only for language-specific phenomena (e.g., splitting “didn’t” into “did” + “n’t”).
• Dynamic Prompt Augmentation: Revised prompt examples are incorporated based on errors observed in expert review (“prompt refinement feedback loop” (Kellert et al., 8 Jun 2025, Zhao et al., 5 Mar 2025)).

Customized prompt templates are indispensable for handling highly agglutinative languages, morphologically complex scripts, or compositional label schemes; they are also the main tool for aligning LLM outputs with formal annotation conventions (e.g., Universal Dependencies).

3. Human-in-the-Loop and Expert Validation

High-quality annotation pipelines universally incorporate human post-processing and expert validation stages, both as an error-correction mechanism and as a source of continual system improvement.

• Gold Standard Comparison: Creation of small, expert-annotated subsets to provide evaluation baselines for LLM outputs (e.g., ∼100 sentences with full manual UD annotation (Kellert et al., 8 Jun 2025)).
• Double Annotation and Agreement Measurement: Systematic two-annotator revision, resolving disagreements per guidelines and calculating inter-annotator agreement (Cohen’s κ up to 0.85).
• Correction and Feedback: Direct correction of head attachments, DEPREL/UPOS assignment, morphological segmentation (as needed), and explicit annotation of “orphan” or “dep” in incomplete CSW utterances.
• Prompt Feedback Loop: Incorporating systematic error patterns back into prompt exemplars and rules, especially for continually surfacing edge cases in under-documented textual phenomena.

This hybrid approach dramatically increases output reliability; for BiLingua Parser, revision lifted LAS from 76.32% (LLM-only) to 95.29% (final, Spanish–English) (Kellert et al., 8 Jun 2025).

4. Evaluation Metrics and Benchmarking

Quantitative evaluation is rigorous and multi-faceted, employing both intrinsic and extrinsic metrics:

• LAS (Labeled Attachment Score):

$$\mathrm{LAS} = \frac{1}{N} \sum_{i=1}^N \mathbf{1}(\hat h_i = h_i^\ast \wedge \hat r_i = r_i^\ast)$$

for head index $h_i$ and DEPREL $r_i$, evaluated per token (Kellert et al., 8 Jun 2025).

• UPOS and DEPREL Accuracy: Token-level matching on universal POS and dependency relations.
• Inter-Annotator Agreement: Cohen’s κ; for Spanish–English code-switching treebank, κ = 0.85 after correction (Kellert et al., 8 Jun 2025).
• Comparative Baselines: Comparison with monolingual and semi-supervised parsers (monolingual LAS ∼70–73%, LLM+expert LAS = 95.29% (Kellert et al., 8 Jun 2025)).
• Coverage and Consistency: In other pipelines, coverage metrics might include attribute/field coverage and consistency with domain experts (e.g., 100% accuracy in clue extraction, 84.1% expert consistency in RiskTagger (Lin et al., 12 Oct 2025)).

The data show that hybrid LLM+human pipelines consistently surpass both manual-only and baseline automated approaches, especially in data-sparse, resource-challenged contexts.

5. Handling Code-Switched and Low-Resource Languages

LLM-based pipelines are particularly suited for annotating code-switched and low-resource data, as demonstrated by BiLingua Parser (Kellert et al., 8 Jun 2025):

• Language Identification: Tokens are pre-tagged for language (e.g., "es", "en", "gn") and tracked through the pipeline. Non-content tokens and punctuations are labeled as "other."
• Heuristic Guidance: Specialized rules accommodate language-pair–specific morphosyntactic features, e.g., splitting contractions ("didn't" → "did", "n't"), repetition handling, orphan dependencies for elliptical spans, and language-conditional head assignments.
• Tokenization Sensitivity: Original segmentation is respected (with the exception of defined contraction expansion).
• Human-Led Refinement: Morphologically complex tokens (e.g., in Guaraní) may be post-processed to better align with UD guidelines.
• Resource Release: The annotated datasets are published under open licenses, offering CoNLL-U exports and guideline documentation for broad reuse.

Such tailored strategies yield robust, reproducible corpora in both well-resourced (Spanish–English) and challenging, under-attested scenarios (Spanish–Guaraní), closing the resource gap for downstream applications.

6. Performance, Scalability, and Future Directions

Empirical benchmarking confirms both quality and efficiency gains. For BiLingua Parser (Kellert et al., 8 Jun 2025), LAS after revision for:

• Spanish–English: 95.29%
• Spanish–Guaraní: 77.42% Standard baselines achieved only 70–73% LAS, and semi-supervised approaches were significantly lower.

Key scalability insights include:

• Few-Shot Prompting Efficiency: Small numbers of in-context exemplars suffice for LLMs to generalize complex syntactic conventions across languages and switching regimes.
• Incremental Improvement Loop: Human-expert feedback is systematically fed back into prompt templates for continual performance gains (also seen in human–LLM collaborative pipelines (Kim et al., 28 Feb 2024)).
• Extension Potential: The modularity of prompt templates and correction feedback enables adaptation to other low-resource languages, codified relation systems, and typologically diverse scenarios.

A plausible implication is that further integration of morphological analyzers, dynamic demonstration selection, and advanced retrieval-augmented prompting will support broader generalization and more nuanced annotation in deeply code-mixed or morphologically rich domains.

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The LLM-based annotation pipeline paradigm leverages the interplay between prompt-driven generative modeling and rigorous human review, with demonstrated ability to bootstrap complex syntactic, semantic, or task-specific corpora in new languages and settings. Key success factors include prompt adaptivity, schema-aware output constraints, iterative expert correction, and a commitment to open resource sharing for community verification and extension.