Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard
Researchers developed FACTGUARD, a multi-agent workflow to create and evaluate questions that are either answerable or unanswerable in long texts. They created FactGuard-Bench, a benchmark with 25,220 examples testing LLMs on recognizing missing or misleading evidence. Results show that training with this data significantly improves models' ability to correctly reject unanswerable questions.