LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
arXiv:2606.29437v1 Announce Type: cross Abstract: The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals human direction, AI contribution, corrections, validation, and traceability. This paper introduces LLMography, a framework for transforming Human-AI co
The rapid proliferation of Large Language Models across various domains necessitates robust methods for accountability and understanding interactions, moving beyond mere output detection.
This framework addresses the critical need for transparency and auditability in human-AI collaboration, fostering trust and enabling better governance of AI systems.
The focus expands from simply identifying AI-generated content to comprehensively analyzing and documenting the entire human-AI interaction process and its influence on outcomes.
- · AI governance & ethics organizations
- · Software engineers using LLMs
- · Educators and academic institutions
- · Regulatory bodies
- · Opaque AI systems
- · Entities avoiding AI accountability
Increased clarity and accountability in the deployment and use of AI assistants across industries.
Development of standardized metrics and tools for evaluating human-AI collaboration dynamics.
Potential for new legal frameworks and compliance requirements based on AI interaction traceability.
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Read at arXiv cs.AI