With the widespread proliferation of the Internet, the spread of fake news has accelerated significantly, evolving from single-text content to multimodal forms that include images and videos. The task of Multimodal Fake News Detection (MFND) takes both text and relevant images as input for fake news identification. However, issues such as image noise and inaccurate focus of visual features often lead to insufficient attention to critical information within images during multimodal fusion. To effectively address these challenges, we propose a covariance matrix-driven image channel allocation method. This method first expands the number of original channel maps, then evaluates the importance of image channels through the covariance matrix and assigns importance scores to the expanded channel maps, thereby redirecting the focus of visual features. Subsequently, we design a multimodal fusion strategy based on a multilayer co-attention mechanism to achieve dynamic fusion across modalities. Finally, a contrastive learning loss is introduced to enhance the alignment between textual and visual modalities. Extensive experiments demonstrate that our method achieves state-of-the-art performance on three public multimodal fake news detection benchmark datasets.

Multimodal content combining textual and visual information poses significant challenges for rumor detection on social media. Compared to traditional spatial domain features, frequency domain features have attracted increasing attention due to their stronger discriminative capabilities. However, existing methods still fall short in capturing cross-modal semantic inconsistencies and often overlook inherent noise in multimodal features, which limits overall detection performance. To address these issues, we propose a novel multimodal rumor detection method based on multi-scale spectral selection and entropy-guided uncertainty fusion. Specifically, we first apply the Discrete Cosine Transform (DCT) to image and text features to convert them into the frequency domain. Then, multi-scale convolutional filters are employed to extract fine-grained information across different frequency scales. Next, modality separation is performed to capture both shared and modality-specific features, enabling more effective cross-modal representation learning. Finally, entropy is used to estimate the uncertainty of each prediction branch, calculate confidence scores, and perform adaptive weighted fusion accordingly. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in multimodal rumor detection, demonstrating stronger detection capability and robustness.

Many studies have shown various biases targeting different demographic groups in language models, amplifying discrimination and harming fairness. Recent parameter modification debiasing approaches significantly degrade core capabilities such as text coherence and task accuracy. And Prompt-based debiasing methods, only effective for predefined trigger words, fail to address deeply embedded stereotypical associations in model parameters. In this paper, we propose BiasUnlearn, a novel model debiasing framework which achieves targeted debiasing via dual-pathway unlearning mechanisms coordinating stereotype forgetting with anti-stereotype retention, while preventing bias polarity reversal through adversarial forget set and dynamic dataset swapping. We conducted extensive experiments with multiple language models across various evaluation benchmarks. The results show that BiasUnlearn outperforms existing methods in mitigating bias in language models while retaining language modeling capabilities. Further experiments reveal that debiasing weights are transferable across model variants, confirming that bias representations become entrenched during pre-training and persist through fine-tuning phases.

In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more training data, and general features directly from LLMs in ICL are not adaptive to the specific downstream task. In this paper, we propose a feature-adaptive and data-scalable in-context learning framework (FADS-ICL), which can leverage task-adaptive features to promote inference on the downstream task, with the supervision of beyond-context samples.Specifically, it first extracts general features of beyond-context samples via the LLM with ICL input form one by one, and introduces a task-specific modulator to perform feature refinement and prediction after fitting a specific downstream task. We conduct extensive experiments on FADS-ICL under varying data settings (4~128 shots) and LLM scale (0.8~70B) settings. Experimental results show that FADS-ICL consistently outperforms previous state-of-the-art methods by a significant margin under all settings, verifying the effectiveness and superiority of FADS-ICL. For example, under the 1.5B and 32 shots setting, FADS-ICL can achieve +14.3 average accuracy from feature adaptation over vanilla ICL on 10 datasets, with +6.2 average accuracy over the previous state-of-the-art method, and the performance can further improve with increasing training data.