Failure of contextual invariance in large language models
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 theoretically irrelevant features, such as the gender of a pronoun for an unrelated referent, become the most informative predictors of model behavior. A Cont...
| Authors | Sagar Kumar, Ariel Flint, Luca Maria Aiello et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 March 2026 |
| Last updated at source | 1 June 2026 |
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