arXiv:2603.23485v2 Announce Type: replace Abstract: Standard evaluation practices assume that large language model (LLM) outputs are stable when prompts are embedded in contextually equivalent discourses. Here, we test this assumption in the setting of gender inference. Using a controlled pronoun selection task, we introduce minimal, theoretically uninformative discourse context and find that this induces large, systematic shifts in model outputs. Correlations with cultural gender stereotypes, present in decontextualized settings, weaken or disappear once context is introduced, while theoretic
The increasing deployment and reliance on LLMs for various applications, especially those involving sensitive inferences, necessitates rigorous testing of their contextual robustness.
This finding highlights a fundamental vulnerability in current LLM evaluation methods and model design, implying that real-world performance may deviate significantly from benchmarked capabilities.
The assumption of contextual invariance in LLM outputs is challenged, requiring a re-evaluation of how models are prompted, tested, and where their outputs can be reliably applied.
- · AI safety researchers
- · Developers of robust LLM evaluation techniques
- · Explainable AI (XAI) solution providers
- · LLM developers relying solely on decontextualized benchmarks
- · Applications making sensitive inferences without contextual awareness
- · Users expecting stable and unbiased LLM outputs across contexts
Immediate re-evaluation of LLM benchmarks and prompt engineering best practices in sensitive domains.
Increased investment in research on contextual understanding and debiasing mechanisms for large language models.
Potential regulatory pressure for LLMs to demonstrate contextual robustness, particularly in public-facing or high-stakes applications.
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