Mind manipulation in dialogue exploits emotional vulnerabilities via covert tactics and hidden goals. We present a fine-grained framework that (i) detects turn-level manipulation tactics and (ii) infers dialogue-level intent before issuing a final manipulation judgment. To support training and evaluation, we curate a balanced dataset of 776 dialogues (388 manipulative, 388 non-manipulative; 15520 dialogue turns) generated via a three-phase multi-agent simulation with dual verification. On this benchmark, our model achieves 84.01% accuracy for dialogue-level manipulation detection, outperforming the strongest baseline by +6.83%. It further attains 76.25% accuracy on tactic detection and 79.53% BERTScore for intent prediction. A user study on forward-simulatability shows +11.0% accuracy improvement when the tactics and intent detected by our model are provided as rationale for dialogue manipulation detection results. These results indicate that explicit, stepwise reasoning over tactics and intent yields both higher performance and actionable interpretability for proactive monitoring of manipulative conversations.

Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models struggle to disambiguate long-tail entities due to their limited training data. Meanwhile, extensively pre-trained large language models (LLMs) possess broader knowledge of uncommon entities. Yet, with a lack of specialized EL training, LLMs frequently fail to generate accurate KB entity names, limiting their standalone effectiveness in EL. With the observation that LLMs are more adept at context generation instead of EL execution, we introduce LLM-Augmented Entity Linking (LLMAEL), the first framework to enhance specialized EL models with LLM data augmentation. LLMAEL leverages off-the-shelf, tuning-free LLMs as context augmenters, generating entity descriptions to serve as additional input for specialized EL models. Experiments show that LLMAEL sets new state-of-the-art results across 6 widely adopted EL benchmarks: compared to prior methods that integrate tuning-free LLMs into EL, LLMAEL achieves an absolute 8.9% gain in EL accuracy. We release our code and datasets.

Stress detection is critical for mental and physical well-being, yet traditional methods such as self-reports and physiological sensors face limitations in efficiency and scalability. Video-based stress detection, leveraging visual cues learned from an annotated video database, offers a non-invasive, cost-effective alternative. However, most models function as black boxes, lacking transparency in their decision-making process, which hinders their trustworthiness. To address this, we propose an interpretable video-based stress detection model that incorporates Chain-of-Thought (CoT) reasoning of large foundation models. Our model follows a structured reasoning chain “Describes Assess-e-Highlight”, mimicking the decision process of psychology experts. To further enhance model reliability, we integrate a self-refinement mechanism that allows the model to reflect on and improve its predictions using Direct Preference Optimization (DPO) to ensure accuracy and faithfulness. Experimental results on two video-based stress detection datasets demonstrate that our approach outperforms state-of-the-art models in both accuracy and interpretability. We release our code at https://github.com/debby1103/stressdetection.git.

Stress haunts people in modern society, which may cause severe health issues if left unattended. With social media becoming an integral part of daily life, leveraging social media to detect stress has gained increasing attention. While the majority of the work focuses on classifying stress states and stress categories, this study introduce a new task aimed at estimating more specific stressors (like exam, writing paper, etc.) through users' posts on social media. Unfortunately, the diversity of stressors with many different classes but a few examples per class, combined with the consistent arising of new stressors over time, hinders the machine understanding of stressors. To this end, we cast the stressor estimation problem within a practical scenario few-shot learning setting, and propose a novel meta-learning based stressor estimation framework that is enhanced by a meta-knowledge inheritance mechanism. This model can not only learn generic stressor context through meta-learning, but also has a good generalization ability to estimate new stressors with little labeled data. A fundamental breakthrough in our approach lies in the inclusion of the meta-knowledge inheritance mechanism, which equips our model with the ability to prevent catastrophic forgetting when adapting to new stressors. The experimental results show that our model achieves state-of-the-art performance compared with the baselines. Additionally, we construct a social media-based stressor estimation dataset that can help train artificial intelligence models to facilitate human well-being.

In recent years, knowledge graph (KG) has attracted significant attention from academia and industry, resulting in the development of numerous technologies for KG construction, completion, and application. XLORE is one of the largest multilingual KGs built from Baidu Baike and Wikipedia via a series of knowledge modeling and acquisition methods. In this article, we utilize systematic methods to improve XLORE's data quality and present its latest version, XLORE 3, which enables the effective integration and management of heterogeneous knowledge from diverse resources. Compared with previous versions, XLORE 3 has three major advantages: (1) We design a comprehensive and reasonable schema, namely XLORE ontology, which can effectively organize and manage entities from various resources. (2) We merge equivalent entities in different languages to facilitate knowledge sharing. We provide a large-scale entity linking system to establish the associations between unstructured text and structured KG. (3) We design a multi-strategy knowledge completion framework, which leverages pre-trained language models and vast amounts of unstructured text to discover missing and new facts. The resulting KG contains 446 concepts, 2,608 properties, 66 million entities, and more than 2 billion facts. It is available and downloadable online at https://www.xlore.cn/ , providing a valuable resource for researchers and practitioners in various fields.

Detecting stress via user's social media posts has attracted increasing research interests in recent years. The majority of the methods leverage word embeddings to represent each of the posted words as a vector, and then perform classification on a sequence of word vectors. To enhance the performance of distinguishing words/phrases related to stressors and stressful emotions from others, in this study, we present a stress-specific word embedding learning framework upon the pre-trained language model BERT. Specifically, we formulate three self-supervised contrastive learning tasks with a joint learning objective. (1) The stressor discrimination task, which is designed to allow the framework to be sensitive to words/phrases about stressors. (2) The stressor cluster discrimination task, which is designed to allow the framework to distinguish stressors into different categories. (3) The stressful emotion discrimination task, which is designed to allow the framework to grasp words/phrases about stressful emotions. Our performance study shows that the learned stress-specific word embedding can significantly benefit social media based stress detection tasks, especially in the more practical scenarios with insufficient labeled data. Besides, we build two user-level social media based stress detection datasets that can help train machine learning models to facilitate human well-being.

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) self-supervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, it advocates the minimum information requirement for self-supervised EA, while we argue that self-described KG's side information (e.g., entity name, relation name, entity description) shall preferably be explored to the maximum extent for the self-supervised EA task. In this work, we propose an interactive contrastive learning model for self-supervised EA. It conducts bidirectional contrastive learning via building pseudo-aligned entity pairs as pivots to achieve direct cross-KG information interaction. It further exploits the integration of entity textual and structural information and elaborately designs encoders for better utilization in the self-supervised setting. Experimental results show that our approach outperforms the previous best self-supervised method by a large margin (over 9% [email protected] absolute improvement on average) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA. The code and data are available at https://github.com/THU-KEG/ICLEA.

The prosperity of massive open online courses provides fodder for plentiful research efforts on adaptive learning. However, current open-access educational datasets are still far from sufficient to meet the need for various topics of adaptive learning. Existing released datasets often cover only small-scale data, lack fine-grained knowledge concepts. They are even difficult to curate and supplement due to platform limitations. In this work, we construct MOOCCubeX, a large, knowledge-centered repository consisting of 4,216 courses, 230,263 videos, 358,265 exercises, 637,572 fine-grained concepts and over 296 million behavioral data of 3,330,294 students, for supporting the research topics on adaptive learning in MOOCs. Licensed by XuetangX, one of the largest MOOC websites in China, we obtain abundant and diverse course resources and student behavioral data and are permitted to make subsequent periodic updates. We propose a framework to accomplish data processing, weakly supervised fine-grained concept graph mining, and data curation to improve usability and richness. Based on the fine-grained concepts, we re-organize the data from the knowledge perspective and acquire more external learning resources from the web. Our repository is now available at https://github.com/THU-KEG/MOOCCubeX.