Privacy concerns have long been a critical issue in AI models. With the rapid advancement of generative AI, the privacy awareness of models has drawn attention, raising new challenges for privacy protection that is independent of data and tasks. This paper introduces a novel framework for enhancing privacy protection through directional steering in representation space, which seamlessly integrates with both language and vision-language models. Specifically, we first construct a comprehensive privacy-related dataset based on the Solove taxonomy of privacy. Then, we leverage this dataset to enhance model privacy awareness in the representation space, steering the model to protect privacy during inference. Experiments on 12 models validate the effectiveness and generalization of our method. Moreover, we demonstrate the transferability of privacy-enhanced representations between same-source large language models (LLMs) and vision-language models (VLMs), offering a scalable solution for privacy protection in frontier AI models.

Pre-trained language models have demonstrated outstanding performance across various natural language understanding tasks. Exploiting the sophisticated understanding capacities of pre-trained language models for humor detection poses a pivotal challenge in the domain of humor recognition. Current methodologies primarily rely on full-parameter fine-tuning of pre-trained language models, often neglecting implicit emotional expressions and incongruity theories inherent in humorous texts. This limitation hinders the effectiveness of humor recognition models. To address these challenges, we propose PromptHR (Prompt-based Humor Recognition), a multi-task learning framework integrating deep commonsense prompt learning and incongruity theory. Specifically, our approach incorporates commonsense knowledge into pre-trained language models through prompt learning, subsequently identifying semantic incongruity through analysis of disparities and interactions between the set-up and punchline in humorous texts. Experimental results show our model achieves state-of-the-art performance on PQA and HAHA tasks, with relative error rate reductions of 12.97% and 17.01%, respectively. This highlights the effectiveness of our approach in pushing the boundaries of humor recognition.

Backdoor attacks pose a significant threat to large language models (LLMs) by embedding malicious triggers that manipulate model behavior. However, existing defenses primarily rely on prior knowledge of backdoor triggers or targets and offer only superficial mitigation strategies, thus struggling to fundamentally address the inherent reliance on unreliable features. To address these limitations, we propose a novel defense strategy, \textit{RepGuard}, that strengthens LLM resilience by adaptively separating abnormal features from useful semantic representations, rendering the defense agnostic to specific trigger patterns. Specifically, we first introduce a dual-perspective feature localization strategy that integrates local consistency and sample-wise deviation metrics to identify suspicious backdoor patterns. Based on this identification, an adaptive mask generation mechanism is applied to isolate backdoor-targeted shortcut features by decomposing hidden representations into independent spaces, while preserving task-relevant semantics. With a multi-objective optimization framework, our method can inherently mitigates backdoor attacks. Across \textit{Target Refusal} and \textit{Jailbreak} tasks under four types of attacks, RepGuard consistently reduced the attack success rate on poisoned data by nearly 80\% on average, while maintaining near-original task performance on clean data. Extensive experiments demonstrate that RepGuard provides a scalable and interpretable solution for safeguarding LLMs against sophisticated backdoor threats.

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs.

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.

Large language models (LLMs) can be used as accessible and intelligent chatbots by constructing natural language queries and directly inputting the prompt into the large language model. However, different prompt' constructions often lead to uncertainty in the answers and thus make it hard to utilize the specific knowledge of LLMs (like ChatGPT). To alleviate this, we use an interpretable structure to explain the prompt learning principle in LLMs, which certificates that the effectiveness of language models is determined by position changes of the task's related tokens. Therefore, we propose MTPrompt, a multi-dimensional task prompt learning method consisting based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets. In addition, we demonstrate the effectiveness and stability of our method in different experimental settings and ablation experiments. In interaction with large language models, embedding more task-related information into prompts will make it easier to stimulate knowledge embedded in large language models.

Humor is a great linguistic tool to express feelings and enhance social bonding. Limited by the diversity of humor expressions and the differential understanding of listeners, automatic detection of humor text is still a difficult and important area in nature language processing. Current methods of humor detection mainly focus on fine-tuning of pre-trained language models, and rarely consider the degree of humor incongruity and knowledge distinction of contextual environments. To alleviate these challenges, we propose SICKNet, a novel multi-tasks learning network based on the incongruity theory of humor and commonsense knowledge. We first utilize the difference between set-up and punchline to detect the semantic incongruity of humor, and next use commonsense knowledge to detect the strength of humorous features. SICKNet achieves the start-of-the-art results on Reddit and TaivopJokes datasets, with accuracy rates of 76.27% and 73.64%, respectively. Our code is available at Github11https://github.com/xing-wei-zeng/SICKNet.

Human comments have become an integral part on evaluating the effectiveness of online courses. Most nature language processing studies consider comments as a composition of statistical texts, which distorts its essence in semantic relation and emotional expression from other disciplines' definition. In order to enlarge its denotation and semantics in cross-discipline perspectives, we firstly define online comments as a complex model that could realize feeling communication, express semantic knowledge, prompt social interaction, and fertilize time character. The social-emotional semantic model (SESM) and its complete construction methods are also introduced to extract comment's social and emotional semantic meaning. Utilizing three user-based and topic-based emotional algorithms, the presented model makes it possible to generate topic-based and learner-based time series. Also, this study evaluates the possibility to visualize SESM on 67084 Chinese MOOC comments and 278 time series. The time-varying phenomenon in double time-series may help teachers determine the reason of the emotion change and then decide to conduct course adjustment or personalized instruction. Future learning analysis on comments should consider multiple semantics and emotional time series.