Alignment Drift Framework for Long-Term Human-LLM Interaction
A recent study introduces a framework centered on mechanisms to explain alignment drift in extended interactions between humans and large language models (LLMs). Alignment drift refers to the gradual shift where the outputs of a system become increasingly influenced by past interactions rather than the current user input, while still maintaining an appearance of coherence and helpfulness. This phenomenon is challenging to identify, as users may perceive an improvement in their experience due to the system's growing familiarity. Previous studies have primarily examined short-term performance, isolated outputs, or singular alignment issues, neglecting the slow, cumulative dynamics. The framework differentiates between signal A and signal B, details the development of drift through feedback loops and sub-pattern selection, categorizes the process into three interaction regimes, and outlines boundary conditions. This paper is available on arXiv under ID 2605.16516.
Key facts
- arXiv paper ID: 2605.16516
- Title: Alignment Drift in Long-Term Human-LLM Interaction: A Mechanism-Oriented Framework
- Alignment drift is a gradual process where outputs become less constrained by current user message and more shaped by prior interaction history
- Drift is difficult to detect because user subjective experience may improve
- Existing research focused on short-term task performance, isolated outputs, or single-instance alignment problems
- Framework defines distinction between signal A and signal B
- Drift develops through feedback loops and sub-pattern selection
- Process divided into three interactional regimes with boundary conditions
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Institutions
- arXiv