Vision-Language Models (VLMs) like CLIP struggle to understand negation, often embedding affirmatives and negatives similarly (e.g., matching "no dog" with dog images). Existing methods refine negation understanding via fine-tuning CLIP’s text encoder, risking overfitting. In this work, we propose CLIPGlasses, a plug-and-play framework that enhances CLIP’s ability to comprehend negated visual descriptions. CLIPGlasses adapts a dual-stage design: a Lens module disentangles negated semantics from text embeddings, and a Frame module predicts context-aware repulsion strength, which is integrated into the modified similarity computation to penalize alignment with negated semantics, thereby reducing false positive matches. Experiments show that CLIP equipped with CLIPGlasses achieves competitive in-domain performance and outperforms state-of-the-art methods in cross-domain generalization. Its superiority is especially evident under low-resource conditions, indicating stronger robustness across domains.

Rumor detection is essential for building a responsible web and internet ecosystem, which has attracted significant attention from the research community. However,emerging topic rumor detection, i.e., identify rumors at the early stages of a topic's emergence where only limited discussions can be observed, still remains a challenge. Technically, this scenario is accompanied by the issues ofdata scarcityon emerging topics and thedata distribution discrepancybetween old topics and emerging new topic. In this paper, we propose a new framework termedLLM-drivenADversarialExampleSynthesis (LADES) for emerging topic rumor detection. LADES utilizes Large Language Models (LLMs) for generating readable and contextually coherent adversarial examples. The generated adversarial examples not only expand the training set to tackle the data scarcity issue, but also act as a bridge to connect the data distribution of old and new topics. To overcome training instability in adversarial example generation, LADES introduces a gradient-free Markov Chain Monte Carlo (MCMC) sampling method. This method ensures adversarial examples are readable and contextually coherent by harnessing LLMs, while promoting effective attacks through entropy-based sampling that targets model uncertainty. To mitigate the impact of potential mislabeling in synthetic data, LADES implements a meta-mixed-learning mechanism. This mechanism dynamically adjusts the weights of synthetic adversarial examples, guided by limited labeled data from emerging topics, thereby alleviating the data noise.

Zhiliang Tian, Jingyuan Huang, Zejiang He, Zhen Huang, Menglong Lu, Linbo Qiao, Songzhu Mei, Yijie Wang, Dongsheng Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

With the emergence of new topics on social media as sources of rumor propagation, addressing the domain shift between the source and target domain and the target domain samples scarcity remains a crucial task in cross-domain rumor detection.Traditional deep learningbased methods and LLM-based methods are mostly focused on the in-domain condition, thus having poor performance in cross-domain setting.Existing domain adaptation rumor detection approaches ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation, resulting in less effective on emerging topic rumor detection.In this paper, we propose a Gradient Coherence guided Meta-Learning approach (GCML) for emerging topics rumor detection.Firstly, we calculate the task generalization score of each source task (sampled from source domain) from a gradient coherence perspective, and selectively learn more "generalizable" tasks that are more beneficial in adapting to the target domain.Secondly, we leverage meta-learning to alleviate the target domain samples scarcity, which utilizes task generalization scores to re-weight metatest gradients and adaptively updates learning rate.Extensive experimental results on realworld datasets show that our method substantially outperforms SOTA baselines.

Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions. However, existing visual text rendering and layout-to-image generation tasks focus on single modality generation or rendering, leaving yet-to-be-bridged gaps between the approaches correspondingly designed for each of the tasks. In this paper, we introduce a unified task called layout-controllable text-object synthesis (LTOS), which merges text rendering and layout-based object generation. We construct a layout-aware text-object synthesis dataset for the LTOS task, containing well-aligned labels visual text, and object information. We also propose a layout-controllable text-object adaptive fusion (TOF) model, which generates images with legible visual text and plausible objects. Specifically, we construct a visual-text rendering to synthesize text and employ an object-layout control module to generate objects while integrating the two modules to harmoniously generate and integrate text and objects in images. To enhance the image-text integration, we propose a self-adaptive cross-attention fusion that forces the image generation to attend more to important text spans. Within such a fusion, we use a self-adaptive learnable factor to learn to control the influence of cross-attention outputs. Experiments show that our model excels the strong baselines and obtain SOTA in LTOS. We release our dataset and codes1.

Large language models (LLMs) have developed rapidly in recent years and have demonstrated excellent performance in various application fields. Despite their outstanding performance, LLMs also introduce significant challenges in the practical inference process, mainly because of their computational and memory-intensive characteristics. Due to the autoregressive nature of the attention mechanism, KV caching can effectively accelerate the inference of LLMs by substituting quadratic-complexity computation with linear-complexity memory accesses. However, during the calculation, it is necessary to transfer the KV cache value to the computing unit, which not only requires a larger storage capacity but also places higher demands on storage bandwidth. In the process of inference and text generation for long texts, storage has become a bottleneck that limits the performance of the inference. In this paper, we first noticed the attention sink phenomenon that high attention scores are allocated towards initial tokens as "attention sink" even if they are not semantically significant in short input sentences and further observed that the attention sink tends to diminish as the length of the input sentence increases. Based on the above preliminary empirical results, we proposed Initial-key Cache, an efficient KV cache selection algorithm that not only effectively addresses the disappearance of the sink phenomenon in long inputs but also comprehensively considers the importance of middle-sentence tokens. We conducted a series of experiments on four baseline models, namely LLaMA, Qwen, Pythia, and OPT, to assess the performance of the Initial-Key Cache. The experiment results indicate that the Initial-Key Cache saves KV cache memory usage, while almost not losing the model’s accuracy.

News Discourse Profiling is a discourse task that aims to recognize the semantic role of each sentence in an article. Within this task, the model not only requires comprehending the news content but also needs to analyze the logical relation in a discourse, posing a great challenge to traditional deep learning techniques. Recently, large language model (LLM) technology has demonstrated significant potential for enhancing the task of news discourse profiling. These models, characterized by their vast number of parameters, excel in logical reasoning and contextual understanding, allowing them to grasp the intricate details and relationships within the text. When paired with the effective chain-of-thought (CoT) technique, LLMs have shown impressive performance in many complex reasoning tasks. However, the discourse structure significantly differentiates between news, thus it is difficult to analyze all news with a unified fixed CoT. In this paper, We propose an adaptive CoT technique for the News Discourse Profiling task, namely Easy-to-Hard Self-Comparative Chain-of- Thought (ESC-CoT). ESC-CoT formulates the task as a multiple-iteration process, i.e., ESC-CoT handles the easiest part at the current iteration and gradually transfers to more complex parts, which can reduce the risk of early error propagation. To alleviate error propagation along the reasoning chain, ESC-CoT compares the conflicting results from different CoT processes, analyzing the context and meaning of sentences more deeply, thereby improving LLM's reasoning ability and reducing the possibility of error. Experiment results demonstrate that ESC-CoT substantially surpasses the traditional news discourse profiling methods. Compared with state-of-the-art (SOTA) methods on two benchmark datasets, the ESC-CoT technique shows an improvement of 7.8 and 19.6 on F1 score, respectively.