Learning When to Trust via Selective Context Preference Optimization

TL;DR - An arXiv paper reframing context robustness as "selective trust": models should reject misleading external context without becoming blind to helpful context, and it introduces a benchmark, a metric, and a DPO-based training method to get there.

• MIST benchmark: human-annotated, rendering each reasoning item under four matched conditions — clean, misleading, correct-context, and irrelevant-context — so trust and resistance can be measured separately.
• SC2W metric: a paired measure counting how often an injected misleading signal flips a clean-correct answer to wrong; a benchmark study finds this susceptibility is universal across models.
• SCOPE method: mines clean-correct/misleading-wrong failure pairs and applies a standard DPO objective over matched preference pairs balanced equally across all four conditions, rather than training only on misleading items.
• Reported effect: substantially lower SC2W on popular open-source models while preserving accuracy when added context is clean, correct, or irrelevant — the paper's core argument being that resistance alone is the wrong evaluation target.