Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Large language model editing methods frequently suffer from overfitting, wherein factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it's contextually inappropriate.To address this challenge, we introduce REACT (Representation Extraction And Controllable Tuning), a unified two-phase framework designed for precise and controllable knowledge editing.In the initial phase, we utilize tailored stimuli to extract latent factual representations and apply Principal Component Analysis with a simple learnbale linear transformation to compute a directional "belief shift" vector for each instance.In the second phase, we apply controllable perturbations to hidden states using the obtained vector with a magnitude scalar, gated by a pre-trained classifier that permits edits only when contextually necessary.Relevant experiments on EVOKE benchmarks demonstrate that REACT significantly reduces overfitting across nearly all evaluation metrics, and experiments on COUNTERFACT and MQuAKE shows that our method preserves balanced basic editing performance (reliability, locality, and generality) under diverse editing scenarios.

Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use of multimodal information is expected to provide more comprehensive information and lead to superior performance. However, the integration of multiple modalities often encounters the modal imbalance problem: since the information in different modalities is unbalanced, optimizing the same objective across all modalities leads to the under-optimization problem of the weak modalities with a slower convergence rate or lower performance. Even worse, we find that in multimodal recommendation models, all modalities suffer from the problem of insufficient optimization. To address these issues, we propose a Counterfactual Knowledge Distillation method that could solve the imbalance problem and make the best use of all modalities. Through modality-specific knowledge distillation, it could guide the multimodal model to learn modality-specific knowledge from uni-modal teachers. We also design a novel generic-and-specific distillation loss to guide the multimodal student to learn wider-and-deeper knowledge from teachers. Additionally, to adaptively recalibrate the focus of the multimodal model towards weaker modalities during training, we estimate the causal effect of each modality on the training objective using counterfactual inference techniques, through which we could determine the weak modalities, quantify the imbalance degree and re-weight the distillation loss accordingly. Our method could serve as a plug-and-play module for both late-fusion and early-fusion backbones. Extensive experiments on six backbones show that our proposed method can improve the performance by a large margin. The source code will be released at https://github.com/CRIPAC-DIG/Balanced-Multimodal-Rec

With the rapid expansion of multimodal content in online social media, automatic detection of multimodal fake news has received much attention. Multimodal joint training commonly used in existing methods is expected to benefit from thoroughly leveraging cross-modal features, yet these methods still suffer from insufficient learning of uni-modal features. Due to the heterogeneity of multimodal networks, optimizing a single objective will inevitably make the models prone to rely on specific modality while leaving other modalities under-optimized. On the other hand, simply expecting each modality to play a significant role in identifying all the rumors is also not appropriate as the multimodal fake news often involves tampering in only one modality. Therefore, how to find the genuine tampering on the per-sample basis becomes the key point to unlock the full power of each modality in a good collaborative manner. To address these issues, we propose aUni-modalEvent-agnosticKnowledgeDistillation framework (UEKD), which aims to transfer knowledge contained in the fine-grained prediction from uni-modal teachers to the multimodal student model through modality-specific distillation. Specifically, we find that the uni-modal teachers simply trained on the whole training set are easy to memorize the event-specific noise information to make a correct but biased prediction, failing to reflect the genuine degree of tampering in each modality. To tackle this problem, we propose to train and validate the teacher models on different domains in training dataset through a cross-validation manner, as the predictions from the out-of-domain teachers can be regarded as event-agnostic knowledge without spurious connections with event-specific information. Finally, to balance the convergence speeds across modalities, we dynamically monitor the involvement of each modality during training, through which we could identify the more under-optimized modalities and re-weight the distillation loss accordingly. Our method could be served as a plug-and-play module for existing multimodal fake news detection backbones. Extensive experiments on three public datasets and four state-of-the-art fake news detection backbones show that our proposed method can improve the performance by a large margin.