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.

Large Language Models (LLMs) have exhibited exceptional performance across diverse domains. However, recent studies reveal that LLMs are plagued by the “reversal curse”. Most existing methods rely on aggressive sample permutation and pay little attention to delving into the underlying reasons for this issue, resulting in only partial mitigation. In this paper, inspired by human knowledge reversal, we investigate and quantify the individual influence of three potential reasons on the reversal curse: 1) knowledge clarity, 2) entity correlation modeling, and 3) pairwise relationship reasoning capability. Motivated by the analysis of these reasons, we propose a novel **P**airwise entity **O**rder- and **R**elationship-**E**nhanced (**PORE**) data strategy, which facilitates bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse. Specifically, PORE augments the samples with entity order-reversal and semantically preserved question-answer pairs, enhancing the encoding of entity correlations in both directions. PORE also employs entity-interleaved pairwise relationship data, which elevates the model’s capability for relationship reasoning. Additionally, to improve the recall of reverse relationships, we leverage knowledge clarity to construct high-clarity data for PORE. Extensive experimental results on available and two newly assembled datasets demonstrate the effectiveness and generalization of our method in both data-sufficient and -constrained situations.

To create a captivating story, a writer often plans a sequence of logically coherent events and ingeniously manipulates the narrative order to generate flashback in place. However, existing storytelling systems suffer from both insufficient understanding of event correlations and inadequate awareness of event temporal order (e.g., go to hospital <after> get ill), making it challenging to generate high-quality events that balance the logic and narrative order of story. In this paper, we propose a narrative order aware framework BPOT (Bidirectional Pretraining Model with Optimal Transport Reward) for story generation, which presents a bidirectional pretrained model to encode event correlations and pairwise event order. We also design a reinforcement learning algorithm with novel optimal transport reward to further improve the quality of generated events in the fine-tuning stage. Specifically, a narrative order aware event sequence model is pretrained with the joint learning objectives of event blank infilling and pairwise order prediction. Then, reinforcement learning with novel optimal transport reward is designed to further improve the generated event quality in the fine-tuning stage. The novel optimal transport reward captures the mappings between the generated events and the sentences in the story, effectively measuring the quality of generated events. Both automatic and manual evaluation results demonstrate the superiority of our framework in generating logically coherent stories with flashbacks.