Born Pragmatic, Trained to Hallucinate? Quantifying the Origins of Contextual Bias in LLMs via the PaCE Benchmark

摘要

While Large Language Models (LLMs) excel at capturing communicative intent, this capability introduces a side effect: Pragmatic Hallucination, where models over-interpret literal contexts to generate non-factual inferences. To quantify this, we introduce the PaCE (Pragmatics-as-Context Evaluation) benchmark, comprising over 3,000 manually verified "context-flip" samples. Evaluations across nine mainstream models reveal a significant Context Sensitivity Gap (CSG), with literal accuracy consistently lagging behind pragmatic reasoning. Attribution analysis indicates that Reinforcement Learning from Human Feedback (RLHF) exacerbates this bias, and neither parameter scaling nor Chain-of-Thought (CoT) fully mitigates it. Crucially, "Strict Prompting" effectively reverses the CSG, demonstrating that the phenomenon stems from behavioral lock-in during training rather than inherent capability deficiencies. Furthermore, error patterns exhibit high systematic correlation across diverse architectures. This study highlights that current alignment paradigms lack precise control over pragmatic boundaries, underscoring the necessity for a "Literal Grounding" mechanism in future safety frameworks.