PRIME: Evaluating Prompt Resolution Under Incompatible Instructions in LLMs
Large language models (LLMs) often encounter conflicting prompts, although current instruction following benchmarks assess those meta-instructions in isolation, limiting the insights about how models process conflicting instructions. We introduce a framework \textit{PRIME}(\textit{Prompt Resolution under Incompatible Meta-Instructions Evaluation}) to analyze behavior of LLMs when provided with conflicting instructions. \textit{PRIME} purposefully produces calibrated conflicts across response length, output format, and reasoning; classifying model responses with a deterministic behavioral taxonomy. We are evaluating five instruction tuned open weight LLMs in two distinct settings, balanced...
| Authors | Tehreem Javed, Shumaim Fatimah, Masooma Bakhtiari et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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