Has there been research on creating intent graphs or behavioral fingerprints using data from interactions with large language models (LLMs)?

1. Intent Detection in the Age of LLMs

A 2024 paper by Gaurav Arora et al. explores how LLMs can be adapted for intent detection in task-oriented dialogue systems

Key contributions include:

• Hybrid modeling: Combining generative LLMs with contrastively fine-tuned sentence transformers (SetFit) to balance accuracy and latency.
• Uncertainty-based routing: A novel method to switch between models based on confidence levels.
• Out-of-scope (OOS) detection: Controlled experiments show that LLMs’ ability to detect OOS intents depends heavily on the scope and granularity of intent labels.
• Internal representation analysis: A two-step method using internal LLM embeddings improved OOS detection F1 scores by over 5% for Mistral-7B.

While this work doesn’t explicitly build “intent graphs,” it lays the groundwork by analyzing how LLMs internally represent and differentiate user intents.

2. LLMs for Intent Taxonomies and Behavioral Modeling

A Microsoft Research paper proposes a framework where LLMs are used to generate, validate, and apply user intent taxonomies based on interaction logs

Highlights include:

• Human-LLM collaboration: LLMs assist in clustering and labeling user intents from web search logs.
• Behavioral signal extraction: The study emphasizes how log data can reveal user satisfaction, goals, and interaction patterns—key components of behavioral fingerprints.
• Scalability: The approach is designed to scale across millions of interactions, making it suitable for enterprise-level applications.

This work is especially relevant to your question, as it bridges LLM interaction data with structured behavioral modeling.

Data Collection and Granularity:

• Vast and Diverse Data: Understanding long-term intent requires analyzing a vast amount of user interaction data across multiple generative AI tools (e.g., text, image, code generation). This data is often diverse in format, content, and context.
• Sensitive Information: User prompts, generated content, and interaction patterns can contain highly sensitive personal, professional, or proprietary information. Collecting this data directly for intent modeling raises significant privacy risks.
• Cross-Platform Silos: Each generative AI tool typically operates independently, storing its own user data. Aggregating this data across platforms to build a holistic view of long-term intent is technically challenging and introduces even greater privacy concerns.
• Ephemeral Interactions: Many generative AI interactions are short-lived. Extracting long-term intent from a series of seemingly disconnected short interactions is difficult without deeper context.

2. Privacy-Preserving Techniques Limitations:

• Trade-off between Privacy and Utility: Techniques like differential privacy, federated learning, and data anonymization are crucial for privacy. However, they often introduce noise or limit the granularity of data, which can degrade the accuracy and richness of intent models. Differential Privacy: While offering strong privacy guarantees, it can significantly impact model performance, especially for complex language tasks, and is computationally intensive for large-scale LLMs. Federated Learning: This allows models to be trained on decentralized data, but it can still be vulnerable to inference attacks where malicious actors try to reconstruct original data from model updates. Data Anonymization: Completely anonymizing data while retaining sufficient utility for long-term intent modeling is extremely difficult, as patterns and correlations can still reveal sensitive information.
• Model Inversion Attacks: Even with privacy-preserving techniques, sophisticated attacks can sometimes reconstruct parts of the training data or infer sensitive information from the model's outputs.
• Explainability and Transparency: "Black-box" generative AI models make it difficult to understand how user intent is being modeled and how privacy is being maintained, hindering trust and accountability.

3. Modeling Long-Term Intent:

• Dynamic and Evolving Intent: User intent is not static; it evolves over time based on new information, experiences, and goals. Modeling this dynamic nature in a privacy-preserving way is challenging.
• Inferring High-Level Trajectories: The goal of inferring "high-level cognitive or professional trajectories" requires sophisticated analysis beyond simple keyword matching. This often involves understanding nuanced context, reasoning, and multi-step processes, which are difficult to capture without direct access to content.
• Context Retention: Maintaining long-term context across multiple interactions and tools is essential for accurate intent modeling, but doing so privately is a significant hurdle.
• Behavioral Fingerprints vs. Direct Content: Relying on embeddings, token patterns, and usage metadata instead of raw prompt content is a good privacy-preserving approach, but extracting robust "behavioral fingerprints" or "intent graphs" from such abstract data is a research frontier.
• Lack of Ground Truth: Defining and verifying "long-term user intent" can be subjective and difficult to establish a definitive ground truth for training and evaluation.