Active Prompting for Information Extraction

• APIE is a dynamic framework that uses adaptive, uncertainty-aware prompts and human-in-the-loop feedback to extract structured data from unstructured inputs.
• It employs modular schema-driven strategies and iterative prompt adjustments to enhance extraction quality in tasks like NER, relation extraction, and event extraction.
• APIE significantly improves extraction performance with empirical F1-score gains and reduced annotation time in biomedical, clinical, and multimodal applications.

Active Prompting for Information Extraction (APIE) refers to a suite of frameworks and methodologies that actively and iteratively guide LLMs or pre-trained LLMs (PLMs) to extract structured information from unstructured input through tailored, dynamically selected, or adaptively constructed prompts and exemplars. APIE explicitly leverages uncertainty signals, schema-specific instructions, user input, or model feedback to optimize the extraction process across a wide range of information extraction (IE) tasks, including named entity recognition (NER), relation extraction (RE), event extraction, template extraction, and document-level information extraction, in both unimodal and multimodal settings.

1. Core Principles and Conceptual Foundation

Active Prompting for Information Extraction fundamentally builds on the insight that model behavior during prompt-based extraction can expose valuable signals regarding areas of confusion, difficulty, or information density. APIE distinguishes itself from passive or static prompting by introducing mechanisms—often inspired by active learning, uncertainty quantification, or human-in-the-loop design—to adaptively select, generate, or refine prompts and in-context exemplars. Central to many leading APIE implementations is the notion of "introspective confusion": the model's own distributional uncertainty across both output structure and semantic content, as quantified through repeated probing or ensemble generation (Zhao et al., 10 Aug 2025).

Another foundational principle is the modularization of both prompts and extraction schemas. This allows for targeted, role- or type-specific instructions (as in joint multi-role prompting for event extraction (Ma et al., 2022), schema-based instructors (Lu et al., 2022), or composable slot filling (Kan et al., 2022)) and the dynamic tailoring of exemplars or instructions to the specific characteristics of a task instance. The explicit inclusion of user-driven or end-user-authored questions (Holzenberger et al., 2022) further broadens the scope, enabling non-experts to define extraction objectives in ways that unlock greater data efficiency.

2. Methodologies for Active Prompting

A diverse array of techniques realizes APIE in practice across the literature:

• Uncertainty-Guided Sample Selection and Prompt Construction: The APIE framework introduced in (Zhao et al., 10 Aug 2025) quantifies model "introspective confusion" using a dual-component uncertainty metric with format uncertainty (adherence to required output schema, e.g., JSON structures) and content uncertainty (consistency across multiple model outputs for the same input). By actively selecting high-uncertainty examples as in-context exemplars, APIE ensures few-shot prompts focus LLM attention on challenging structured extraction cases.

$$U_\text{total} = \alpha\, U_\text{disagreement} + \beta\, U_\text{format} + \gamma\, U_\text{content}$$

where $U_\text{disagreement}$ is output variability, $U_\text{format}$ penalizes deviations from format, and $U_\text{content}$ reflects semantic inconsistency (Zhao et al., 10 Aug 2025).

• Human-in-the-loop and Feedback-driven Refinement: InteractiveIE (2305.14659) proposes a pipeline where unsupervised question generation induces template slots, which are then refined by targeted human feedback. This approach amplifies extraction quality with minimal annotation by integrating human edits into clustering and slot definition processes.
• Self-consistency and Active Learning Strategies: The APE tool (Qian et al., 29 Jul 2024) employs self-consistency-based sampling, identifying ambiguous (high entropy) examples—calculated as

$$H(p) = -R^+(p) \log R^+(p) - (1-R^+(p)) \log(1-R^+(p))$$

—to iteratively populate prompts for entity matching. This accelerates the discovery of informative few-shot examples.

• Schema-adaptive and Modular Prompting: The UIE system (Lu et al., 2022) and unified composable frameworks (Kan et al., 2022) utilize explicit schema prompts, enabling the LLM to condition its output on variable, task-specific targets through directives like "[spot] person [spot] company [asso] work for [text]". The modular prompt design in business process modeling (Neuberger et al., 26 Jul 2024) divides prompts into context description, meta language task definitions, and restriction modules for improved robustness and universal applicability.
• Role of Explicit Format Constraints: By encoding required output formats as part of the prompt (e.g., through strict JSON schemas, see (Khatami et al., 5 Dec 2024)), APIE ensures more parseable, reliable extraction, especially in domains where output variability must be minimized (e.g., biomedical (Nagar et al., 22 Aug 2024), clinical (Zhang et al., 22 May 2025)).

3. Impact on Information Extraction Performance

The APIE paradigm yields notable improvements in information extraction across multiple domains and model architectures. For example, introspective confusion-driven APIE consistently yields state-of-the-art F1-scores across four standard benchmarks (ACE04-NER, CoNLL03, CoNLL04, SciERC) compared to random or static few-shot selection strategies (Zhao et al., 10 Aug 2025). The PAIE framework demonstrates F1 gains of 3.5% and 2.3% on three event extraction benchmarks for base and large models, respectively (Ma et al., 2022). Modular, schema-driven prompting achieves up to 8% absolute F1 improvement in relation extraction for process modeling tasks and reduces annotation time in clinical dense IE by about 24 hours for 138 cases (Neuberger et al., 26 Jul 2024, Zhang et al., 22 May 2025).

Cross-lingual open IE benefits from multi-stage tuning coupled with two-stage chain-of-thought prompting for high-quality data augmentation and language-specific adapter modules, resulting in improved F1-scores (up to ~1.6% in some languages over prior models) (Li et al., 2023).

In dense clinical IE, category-specific and subheading-filtered prompting improve alignment and extraction fidelity, with open-source models like Qwen2.5-7B surpassing GPT-4o on benchmark datasets (Zhang et al., 22 May 2025). Furthermore, active prompting outperforms generic advanced prompting methods such as chain-of-thought and RAG in biomedical structured extraction scenarios, where adherence to strict output schemas is critical (Nagar et al., 22 Aug 2024).

4. Taxonomy of Prompting Strategies and Design Considerations

Approach	Key Features	Use Cases
Introspective Confusion-based	Dual uncertainty scoring (format + content); dynamic exemplar selection	Structured span extraction, relation extraction (Zhao et al., 10 Aug 2025)
Schema-driven Adaptive Prompting	Prepend schema (SSI or composable sub-prompts); modular slot design	Universal IE (NER, RE, events, sentiment) (Lu et al., 2022, Kan et al., 2022)
Question-based Prompting	End-user or expert-generated natural language questions	Template extraction, few-shot regimes (Holzenberger et al., 2022)
Human-in-the-loop Feedback	Question generation, clustering, interactive schema refinement	Biomedical/legal document IE (2305.14659)
Output Format Enforcement	Strict formatting, e.g., JSON schemas	ABM extraction, code generation (Khatami et al., 5 Dec 2024), Business Process (Neuberger et al., 26 Jul 2024)
Self-consistency-based Sampling	Entropy-guided ambiguous example selection	Prompt engineering, entity matching (Qian et al., 29 Jul 2024)
Subheading/category-specific prompts	Category-tailored extracted context windows, filtered input	Dense clinical extraction (Zhang et al., 22 May 2025)

Considerations when deploying APIE include:

• Balancing format strictness and content guidance to avoid over-constraining the LLM.
• Robustness to prompt wording and few-shot example selection; active, data-driven selection strategies are favored.
• Integration with domain-adaptive pretraining, particularly for low-resource or specialized medical/clinical settings (Richter-Pechanski et al., 20 Mar 2024, Zhang et al., 22 May 2025).
• Potential for users without NLP expertise to effectively contribute robust prompts, broadening access (Holzenberger et al., 2022).

5. Extension to Multimodal and Cross-lingual Extraction

Recent work extends APIE to multimodal and cross-lingual scenarios:

• Multimodal Information Extraction: Unified pipelines based on multimodal question answering (MQA) decompose tasks into span extraction and multi-choice QA, aligning the extraction formulation with vision-LLM pretraining. This dramatically increases performance over vanilla prompting (+10–+37 percentage points F1 in various benchmarks) and enables robust transfer between image/text event, entity, and relation extraction tasks (Sun et al., 2023).
• Vision Document Target Prompting: For document images, target prompting restricts VLM attention to precisely identified sub-regions, yielding more accurate, less noisy outputs in real-world automation and data entry tasks (Medhi, 7 Aug 2024).
• Cross-lingual Transfer and Multi-stage Prompting: Coupling disentangled multi-stage fine-tuning, per-language modular adapters (mixture-of-LoRAs), and chain-of-task prompting (translation plus annotation) supports robust, language-agnostic open IE capabilities in multilingual settings (Li et al., 2023).

6. Challenges, Limitations, and Design Robustness

Despite its demonstrated utility, APIE faces several limitations:

• Highly structured domains (e.g., biomedical) expose a mismatch between generic advanced prompting methods (chain-of-thought, self-consistency, RAG) and the strict formatting required for precision extraction; standard prompting with direct constraints can outperform more elaborate reasoning strategies (Nagar et al., 22 Aug 2024).
• All evaluated LLMs may underperform in extracting negative findings (e.g., signs, symptoms not present)—an issue for clinical decision support (Zhang et al., 22 May 2025).
• The engineering of prompts (e.g., format examples, specificity of definitions) materially affects extraction precision and model robustness; prompt ablations suggest the importance of clear format examples and output constraints (Neuberger et al., 26 Jul 2024).
• Human-in-the-loop designs, though powerful, require careful balancing between automation and required supervision to ensure scalability and schema alignment (2305.14659).
• Annotation and computational cost can grow with sentence-level or per-instance prompting (as in sentence-based extraction (Hsu et al., 18 Nov 2024)); batch processing tradeoffs must be considered.

7. Domain adaptation, Tooling, and Broader Impact

Active Prompting for Information Extraction has facilitated the creation of extensible tools and frameworks, such as LLM-IE (Hsu et al., 18 Nov 2024), which includes interactive schema and prompt editors, per-sentence prompting, and integrated visualization modules for healthcare extraction pipelines.

CNL-P (Xing et al., 9 Aug 2025) introduces controlled natural language prompts formalized with BNF-style grammars and static analysis tools, treating prompts as software-like APIs. A conversion tool (NL2CNL-P) lowers the barrier for users unfamiliar with formal specification, and a linting tool catches syntactic and semantic prompt errors before runtime, thus bridging prompt engineering and software engineering best practices for robust human-AI interaction.

In summary, Active Prompting for Information Extraction encompasses a spectrum of adaptive, data-driven, and user-steered techniques for optimizing model-guided structured information extraction, substantiated by strong empirical gains in accuracy, interpretability, and scalability across domains, languages, and modalities. APIE’s evolution is tightly coupled to advances in uncertainty quantification, schema formalization, human-guided formal specification, and the integration of dynamic prompt/feedback loops, laying the groundwork for increasingly flexible and reliable intelligent information extraction systems.