While Large Language Models (LLMs) are trained for factual accuracy, this objective can directly conflict with the critical demand for source fidelity. This paper isolates and formalizes this conflict as Harmful Factuality Hallucination (HFH): a previously overlooked failure mode where an LLM’s attempt to “correct” perceived source errors results in an output that is factually true but unfaithful to the input. Unlike traditional hallucination research focused on models generating falsehoods, we investigate the harm of misplaced correctness. We introduce a reproducible framework to elicit and measure HFH using controlled entity-level perturbations (both soft, embedding-based and hard, instruction-based) paired with strategic entity selection. Across summarization, rephrasing, and QA tasks, our evaluation of diverse LLMs reveals that HFH is a prevalent behavior that worsens with model scale. We identify three underlying mechanisms and demonstrate that a simple instructional prompt can reduce HFH rates by approximately 50%. Our framework turns the abstract factuality–faithfulness tension into a measurable, actionable target for building more reliable LLM systems. Our code is publicly available at https://github.com/ResponsibleAILab/Harmful-Factuality-Hallucination.

Long-context understanding is crucial for many NLP applications, yet transformers struggle with efficiency due to the quadratic complexity of self-attention. Sparse attention methods alleviate this cost but often impose static, predefined masks, failing to capture heterogeneous attention patterns. This results in suboptimal token interactions, limiting adaptability and retrieval accuracy in long-sequence tasks. This work introduces a dynamic sparse attention mechanism that assigns adaptive masks at the attention-map level, preserving heterogeneous patterns across layers and heads. Unlike existing approaches, our method eliminates the need for fine-tuning and predefined mask structures while maintaining computational efficiency. By learning context-aware attention structures, it achieves high alignment with full-attention models, ensuring minimal performance degradation while reducing memory and compute overhead. This approach provides a scalable alternative to full attention, enabling the practical deployment of large-scale Large Language Models (LLMs) without sacrificing retrieval performance. DAM is available at: https://github.com/HanzhiZhang-Ulrica/DAM.

Prompt privacy is crucial, especially when using online large language models (LLMs), due to the sensitive information often contained within prompts. While LLMs can enhance prompt privacy through text rewriting, existing methods primarily focus on document-level rewriting, neglecting the rich, multi-granular representations of text. This limitation restricts LLM utilization to specific tasks, overlooking their generalization and in-context learning capabilities, thus hindering practical application. To address this gap, we introduce DP-GTR, a novel three-stage framework that leverages local differential privacy (DP) and the composition theorem via group text rewriting. DP-GTR is the first framework to integrate both document-level and word-level information while exploiting in-context learning to simultaneously improve privacy and utility, effectively bridging local and global DP mechanisms at the individual data point level. Experiments on CommonSense QA and DocVQA demonstrate that DP-GTR outperforms existing approaches, achieving a superior privacy-utility trade-off. Furthermore, our framework is compatible with existing rewriting techniques, serving as a plug-in to enhance privacy protection. Our code is publicly available at anonymous.4open.science for reproducibility.