Knowledge editing aims to efficiently update factual information in Large Language Models (LLMs) without full retraining. However, existing methods still suffer from performance degradation in batch knowledge editing. We identify that semantic representation entanglement, such as overlapping concepts and shared syntactic patterns, accumulates interference in the representation space and reduces editing precision. To bridge this gap, in this paper, we propose Orthogonal Representation Editing (ORE), which performs edits in the hidden representation space of LLMs by constructing a general semantic subspace and enforcing orthogonal constraints on edit vectors, effectively decoupling semantic entanglement. Furthermore, we introduce a gated non-linear representation head to enable adaptive learning of editing locations and precise control over knowledge injection. Extensive experiments show that ORE outperforms existing methods and achieves superior performance in cross-lingual knowledge editing scenarios. We release our code at https://github.com/YVVH/ORE.

LLMs have improved the fluency and informativeness of abstractive summarization but remain prone to hallucinations, where generated content deviates from the source document. Recent PMI decoding strategies mitigate over-reliance on prior knowledge by comparing output probabilities with and without source documents, effectively enhancing contextual utilization and improving faithfulness. However, existing strategies often neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge, limiting their flexibility. In this work, we propose Salience-Aware Reinforced Adaptive decoding (SARA), which incorporates salient information and allows the model to adaptively determine reliance on the source document’s context, salient context, and the model’s prior knowledge based on pointwise mutual information. Moreover, a tokenwise adaptive decoding mechanism via reinforcement learning is proposed in SARA to dynamically adjust the contributions of context and prior knowledge at each decoding timestep. Experiments on CNN/DM, WikiHow, and NYT50 datasets show that SARA consistently improves the quality and faithfulness of summaries across various LLM backbones without modifying their weights.

While Large Language Models (LLMs) excel in diverse domains, their validity in event reasoning remains underexplored. Most existing works merely stagnate at assessing LLMs’ event reasoning with a single event relational type or reasoning format, failing to conduct a complete evaluation and provide a practical solution for capability enhancement. In this paper, we propose PIPER, the first comprehensive benchmark for Probing Into the Performance boundary of LLMs in Event Reasoning. Motivated by our evaluation observations and error patterns analysis, we meticulously craft 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity. Additionally, a novel Debiasing and Distillation-Enhanced Supervised Fine-Tuning (D²E-SFT) strategy is presented, which facilitates adhering to context and fixating significant contextual event information to elevate the event reasoning capability. Specifically, D²E-SFT removes the given sample’s context to construct an imagined sample, subtracting its logits to mitigate the bias of neglecting context and improve contextual faithfulness. To guide the model in emphasizing significant contextual event information, D²E-SFT employs a context-refined sample to achieve self-distillation with the alignment of logits. Extensive experimental results demonstrate the effectiveness of our data and strategy in expanding the performance boundary of event reasoning.