Towards Reliable Long-Context Reasoning: Detecting Unanswerable Questions via FactGuard

arXiv:2504.05607v2 Announce Type: replace-cross Abstract: Despite the success of Large Language Models (LLMs) in long-context tasks, their reliability in recognizing unanswerable questions remains a critical challenge. In information-seeking scenarios, models often suffer from overconfidence and hallucinations when evidence is missing or misleading. In this paper, we propose FACTGUARD, a systematic multi-task workflow for generating evidence-grounded answerable and unanswerable questions. We introduce FACTGUARD-BENCH, a benchmark comprising 25,220 examples with context lengths up to 128K, spec

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