arXiv:2606.13944v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly characterised in recent evaluation work as having stable, model-level preference and value systems. However, accompanying robustness checks are limited to incidental prompt perturbations such as syntax variation and option reordering. This leaves open whether the measured properties survive when the surrounding task context changes, as it does in most real deployments. We test this directly across two established pairwise paradigms: ranking country preferences and eliciting utility judgements. In both
This research is emerging now as LLMs transition from research curiosities to widely deployed systems impacting various real-world contexts, necessitating a deeper understanding of their adaptability.
A strategic reader should care because understanding how LLM preferences shift with context is critical for reliable and ethical deployment, affecting use cases from enterprise to sensitive geopolitical applications.
The prior assumption of stable, model-level LLM preferences is challenged, indicating that their 'values' are more fluid and dependent on deployment context than previously understood.
- · AI ethics and safety researchers
- · Developers of contextual AI frameworks
- · Companies offering bespoke LLM fine-tuning
- · Generic 'one-size-fits-all' LLM deployments
- · Companies relying on static LLM evaluations
LLM evaluations will adopt more dynamic, contextual robustness checks beyond simple prompt variations.
AI governance and policy will need to account for context-dependent LLM behavior, complicating regulatory frameworks.
The concept of 'alignment' for LLMs may shift from a fixed state to a dynamically managed and monitored process responsive to deployment environments.
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Read at arXiv cs.CL