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Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation

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arXiv 2025年

作者： Li, Xiangju Yang, Dong Zhu, Xiaogang Huang, Faliang Zhang, Peng Zhao, Zhongying College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266500 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China

Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method’s effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://***/zxgnlp/InstruDa-LLM. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

StructVizor: Interactive Profiling of Semi-Structured Textual Data 25

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StructVizor: Interactive Profiling of Semi-Structured Textua...

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Yanwei Huang Yan Miao Di Weng Adam Perer Yingcai Wu State Key Lab of CAD&CG Zhejiang University Hangzhou Zhejiang China School of Software Technology Zhejiang University Ningbo Zhejiang China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

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DRDM: A Disentangled Representations Diffusion Model for Syn...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Enbo Huang Yuan Zhang Faliang Huang Guangyu Zhang Yang Liu Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photorealistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM

关键词： Limbs Image synthesis Shape Clothing Semantics Production Feature extraction Diffusion models Sampling methods Speech processing

ExplainitAI: When do we trust artificial intelligence? The influence of content and explainability in a cross-cultural comparison

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arXiv 2025年

作者： Kang, Sora Potinteu, Andreea-Elena Said, Nadia Human-Computer Interaction+Design Lab Seoul National University Seoul Korea Republic of Perception and Action Lab Leibniz-Institut für Wissensmedien Tübingen Germany Applied Cognitive Psychology Department of Psychology University of Tübingen Tübingen Germany

This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitAI, we systematically examined these factors with quota-based samples from both countries (N = 297). Our findings revealed significant cultural distinctions: Korean participants exhibited higher trust, more positive user experience ratings, and more favorable perception of AI compared to German participants. Additionally, topic dependency was a key factor, with participants reporting lower trust in AI when addressing societally debated topics (e.g., migration) versus health or entertainment topics. These perceptions were further influenced by interactions among cultural context, content domains, and explainability conditions. The result highlights the importance of integrating cultural and contextual nuances into the design of AI systems, offering actionable insights for the development of culturally adaptive and explainable AI tailored to diverse user needs and expectations across domains. © 2025, CC BY-NC-SA.

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Future Self Agents to Enhance Young Adults’ Career Exploration

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arXiv 2025年

作者： Jeon, Hayeon Yoon, Suhwoo Lee, Keyeun Kim, Seo Hyeong Kim, Esther Hehsun Cho, Seonghye Ko, Yena Yang, Soeun Dabbish, Laura Zimmerman, John Kim, Eun-Mee Lim, Hajin hci+d lab. Seoul National University Korea Republic of Department of Communication Seoul National University Korea Republic of Human-Computer Interaction Institute Carnegie Mellon University United States HCI Institute Carnegie Mellon University United States

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants’ future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants’ engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults’ career exploration. Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration 25

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Letters from Future Self: Augmenting the Letter-Exchange Exe...

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hayeon Jeon Suhwoo Yoon Keyeun Lee Seo Hyeong Kim Esther Hehsun Kim Seonghye Cho Yena Ko Soeun Yang Laura Dabbish John Zimmerman Eun-mee Kim Hajin Lim hci+d lab. Seoul National University Seoul Republic of Korea Department of Communication Seoul National University Seoul Republic of Korea Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA HCI Institute Carnegie Mellon University Pittsburgh Pennsylvania USA

Towards Understanding Interactive Sonic Gastronomy with Chefs and Diners 25

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Towards Understanding Interactive Sonic Gastronomy with Chef...

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Hongyue Wang Jialin Deng Linjia He Nathalie Overdevest Ryan Wee Yan Wang Phoebe O. Toups Dugas Don Samitha Elvitigala Florian Floyd Mueller Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Diverse-ability Interaction Lab School of Computer Science Bristol United Kingdom Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia and Mineral Resources Commonwealth Scientific and Industrial Research Organisation Melbourne Australia