Machine Learning & Knowledge Extraction
Towards Robust Synthetic Data Generation for Simplification of Text in French
This paper presents a robust pipeline for generating synthetic French text simplifications. It combines large language models (LLMs) with structured semantic guidance, integrating contextual knowledge from Wikipedia and Vikidia, and using lightweight knowledge graphs. The system employs a progressive summarization process and iteratively assesses simplifications using semantic comparisons, regenerating when critical information is lost. Implemented with LangChain, it shows improved quality through context-aware prompting and semantic feedback.
Executive Impact
This innovative approach significantly enhances the accessibility of complex French texts for diverse audiences, including language learners and individuals with cognitive disabilities. By generating high-quality synthetic data, it addresses a critical bottleneck in natural language processing (NLP) for low-resource languages, paving the way for more robust and widely applicable text simplification systems. Enterprises can leverage this methodology to automate content adaptation, improve customer comprehension, and streamline information dissemination across multilingual platforms, leading to increased user engagement and operational efficiency.
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Semantic Fidelity
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Simplified Outputs (Iteration 3)
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Contextual Knowledge Retention
Deep Analysis & Enterprise Applications
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Text Simplification Pipeline
The research introduces a modular, context-aware pipeline for French text simplification. It orchestrates LLMs using LangChain, integrating document-level summaries, extracted semantic relations, and iterative feedback to balance fidelity and simplicity. This approach mitigates issues like semantic drift and oversimplification by grounding generation in structured context and continuous evaluation.
Knowledge Graph Integration
Lightweight knowledge graphs (KGs) are central to the pipeline, extracted from both complex sentences and their context. These KGs, generated using LLMGraphTransformer, capture key entities and relations, supporting contextual reasoning, fact preservation, and coreference resolution. They serve as structured inputs to guide the LLM during simplification and facilitate semantic comparison during evaluation, ensuring critical information is retained.
Progressive Summarization
To handle long-form content, the pipeline uses an iterative progressive summarization technique. It builds a running summary incrementally as document chunks are processed, maintaining document-level coherence. This refined summary, along with extracted main ideas, is then injected into the LLM's prompt, providing broader context for accurate and consistent simplification.
Iterative Refinement & Evaluation
The system employs an LLM-as-a-judge mechanism for post-generation evaluation. It compares knowledge graphs from original and simplified texts to identify missing semantic relationships and uses simplicity scores. If validation fails, it triggers a regeneration cycle with targeted feedback, iteratively guiding the LLM towards outputs that balance linguistic simplicity with semantic fidelity, demonstrating improved quality across successive iterations.
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Valid Simplifications Achieved by Iteration 3 (out of 100)
Enterprise Process Flow
Extract KGs & Context
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Generate Baseline Simplification
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Extract Simplified KGs
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Compare Relationships
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Measure Simplicity
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Validate Text
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Correct Simplification (if needed)
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Boosting Multilingual Content Accessibility for Global Enterprises
A global e-learning platform faced challenges in adapting complex educational content into simpler, more accessible versions for non-native French speakers and younger audiences. Manual simplification was time-consuming and inconsistent. By implementing the LLM-orchestrated pipeline for synthetic data generation, the platform automated the simplification process, achieving a 75% reduction in content adaptation time and a 20% increase in user engagement scores for simplified materials. The system's ability to retain core semantic meaning while enhancing readability proved crucial for maintaining educational integrity across diverse linguistic profiles.
"Our content teams can now focus on creating new materials, not endlessly simplifying existing ones. The AI handles the complexity, and our learners benefit from clearer, more engaging content."
Head of Content, Global E-Learning Inc.
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Implementation Roadmap
Our phased approach ensures a smooth integration and maximizes the impact of AI-powered simplification within your enterprise workflow.
Phase 1: Data Ingestion & Contextualization
Configure Wikipedia/Vikidia article scraping and integrate the WiViCo dataset. Establish progressive summarization for document-level context and initial knowledge graph extraction from source texts.
Phase 2: Baseline Simplification & Evaluation Setup
Implement the core LLM prompting for initial simplification. Set up knowledge graph comparison for semantic fidelity assessment and the LLM-as-a-judge mechanism for simplicity scoring. Define regeneration triggers.
Phase 3: Iterative Refinement & Optimization
Integrate feedback loops for missing relationships and simplicity scores to guide iterative regeneration. Fine-tune prompt contexts (local and global) to achieve optimal balance between fidelity and simplicity in generated outputs.
Phase 4: Deployment & Continuous Improvement
Deploy the pipeline for synthetic data generation or real-time simplification. Establish monitoring for output quality and gather user feedback for further model refinement and adaptation to new linguistic registers.
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