MDL-Based Survey Motif and Synthetic Data Generation Project Proposal

Overview

This project leverages the Minimum Description Length (MDL) principle to discover recurring symbolic motifs in structured and unstructured survey responses. These motifs serve as the basis for generating high-fidelity synthetic data, enabling public experimentation without exposing sensitive content. The project combines principles from compression theory, natural language processing, and human-in-the-loop validation to build a robust motif discovery and synthetic text generation pipeline.

Motivation

Many survey responses—especially those involving sensitive topics such as domestic violence or drug and alcohol abuse—contain personally sensitive or emotionally charged information. Open experimentation using such real data is not viable. Synthetic data provides a privacy-preserving alternative, but to be useful, it must faithfully preserve thematic, stylistic, and structural properties of the original responses.

This project uses MDL-based motif discovery as a proxy for identifying recurring and semantically significant patterns across survey responses. These motifs are then used to generate synthetic datasets that retain the informational essence of the originals while abstracting away individual identifiers.

Key Concepts

MDL and Symbolic Motif Discovery

We apply MDL to identify symbolic motifs—recurring semantic expressions or themes—that result in the greatest reduction of overall corpus size when replaced with compressed representations. This goes beyond frequency analysis, identifying motifs that reduce the total description length of the dataset and are therefore assumed to be semantically meaningful.

Compression as a Proxy for Semantic Significance

In the MDL framework, motifs that contribute more to data compression are considered more semantically meaningful. This principle draws on insights from Kolmogorov Complexity and algorithmic information theory: if a motif helps compress a large number of responses, it likely represents a shared theme or concept across respondents.

Textual Intelligence as Semantic Pattern Recognition

“Textual intelligence” in this project refers to the system’s capacity to identify abstract, semantically coherent motifs across text fields—going beyond simple token proximity or repetition. This involves recognizing recurring patterns of meaning and compressible themes across survey responses that are structurally or contextually related, even when lexically dissimilar.

Synthetic Data Generation via Motif Recombination

Synthetic survey responses are generated by probabilistically recombining previously discovered motifs, preserving thematic structure without reproducing any original respondent’s full text. This ensures data privacy while maintaining the analytical utility of the dataset.

Theme Traceability and Human-in-the-Loop Validation

Motifs are linked to survey question IDs, enabling structured theme extraction and downstream analytics pipelines. Human analysts play a critical role in assigning interpretive labels to motifs and refining automatically extracted themes. This step ensures that generated themes are contextually appropriate and ethically suitable for policy or research purposes.

Phased Implementation Plan

Phase 1: Semantic MDL Pipeline on Synthetic Playground

Build motif discovery pipeline using a hybrid MDL-semantic clustering strategy.
Use contextual embeddings and LLM-based abstraction to identify compressible semantic units.
Reconstruct synthetic responses from recombined motifs to validate thematic fidelity.
Emphasize semantic proximity and symbolic abstraction as key components of textual intelligence.
Run on synthetic sample data in open environments (e.g., free-tier Google Colab).

Phase 2: Scaling and Traceability

Associate motifs with specific questions (for explainability).
Extract and visualize themes from motif mappings.
Create interactive motif/theme browser.

Phase 3: Human-Centric Theme Annotation

Develop UI for analysts to review motif-grouped text.
Implement annotation workflow to label canonical themes.
Refine and validate motif-to-theme mappings with expert input.

Phase 4: Production-Ready Synthetic Dataset

Apply refined motif set to large-scale synthetic data generation.
Validate distributional similarity with original dataset.
Package dataset and pipeline for broader usage and reproducibility.

Comparison: Kolmogorov Complexity (KC) vs. Minimum Description Length (MDL)

Aspect	Kolmogorov Complexity (KC)	Minimum Description Length (MDL)
Definition	Length of shortest program producing the data	Length of model + data encoded using the model
Interpretability	Theoretically grounded but uncomputable	Practical approximation of KC
Use in this project	Theoretical underpinning for motif complexity	Practical compression-based motif discovery
Semantic implication	Complex = meaningful if non-compressible	Compressible = meaningful if recurring across dataset
Utility in motif detection	Less practical for motif extraction	Central mechanism for motif discovery

Conclusion

This project demonstrates that compression-based motif discovery, when guided by MDL and augmented with semantic modeling, is a powerful tool for extracting symbolic, semantically-rich patterns from text. These motifs not only compress but also characterize the underlying themes in sensitive survey data, supporting the generation of synthetic responses with traceable and interpretable structure.

By grounding this work in MDL and defining textual intelligence as semantic abstraction and pattern recognition, the project offers a scalable, privacy-respecting approach to textual analysis that aligns with modern AI ethics and policy workflows.