10 citations · 20 across the 22 of their papers we have counts for
25 papers
WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection
Xian Li, Yuanhe Tian, Yang Yang +2
Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through risk-post selection, sympto…
HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection
Danyu Sun, Jinghuai Zhang, Yuan Tian +1
Recent benchmark efforts have advanced the evaluation of large language models (LLMs) in cybersecurity, including tasks such as penetration testing and vulnerability identification…
SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure
Chen Su, Pengsen Cheng, Yuanhe Tian +1
Public events on social media generate large volumes of discussion whose collective dynamics carry direct value for opinion forecasting and crisis response. Capturing how these dyn…
Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework
Quan Zheng, Yuanhe Tian, Ming Wang +1
In the digital era, effective identification and analysis of verbal attacks are essential for maintaining online civility and ensuring social security. However, existing research i…
Explainable Multimodal Aspect-Based Sentiment Analysis with Dependency-guided Large Language Model
Zhongzheng Wang, Yuanhe Tian, Hongzhi Wang +1
Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, which is essential for fine-grained…
Chain-of-thought Reviewing and Correction for Time Series Question Answering
Chen Su, Yuanhe Tian, Yan Song
With the advancement of large language models (LLMs), diverse time series analysis tasks are reformulated as time series question answering (TSQA) through a unified natural languag…
MoE Pathfinder: Trajectory-driven Expert Pruning
Xican Yang, Yuanhe Tian, Yan Song
Mixture-of-experts (MoE) architectures used in large language models (LLMs) achieve state-of-the-art performance across diverse tasks yet face practical challenges such as deployme…
Frustratingly Easy Task-aware Pruning for Large Language Models
Yuanhe Tian, Junjie Liu, Xican Yang +2
Pruning provides a practical solution to reduce the resources required to run large language models (LLMs) to benefit from their effective capabilities as well as control their cos…
Text Reinforcement for Multimodal Time Series Forecasting
Chen Su, Yuanhe Tian, Yan Song +1
Recent studies in time series forecasting (TSF) use multimodal inputs, such as text and historical time series data, to predict future values. These studies mainly focus on develop…
ChiMed 2.0: Advancing Chinese Medical Dataset in Facilitating Large Language Modeling
Yuanhe Tian, Junjie Liu, Zhizhou Kou +2
Building high-quality data resources is crucial for advancing artificial intelligence research and applications in specific domains, particularly in the Chinese medical domain. Exi…
Evaluation of Coding Schemes for Transformer-based Gene Sequence Modeling
Chenlei Gong, Yuanhe Tian, Lei Mao +1
Currently, many studies view DNA sequences as a special type of language and utilize Transformers to model them. These studies use fixed-length k-mer segmentation and BPE subword t…
Fusing Large Language Models with Temporal Transformers for Time Series Forecasting
Chen Su, Yuanhe Tian, Qinyu Liu +2
Recently, large language models (LLMs) have demonstrated powerful capabilities in performing various tasks and thus are applied by recent studies to time series forecasting (TSF) t…
Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis
Junjie Liu, Yuanhe Tian, Yan Song
Aspect-based sentiment analysis (ABSA) is a crucial fine-grained task in social media scenarios to identify the sentiment polarity of specific aspect terms in a sentence. Although…
Computed Tomography Visual Question Answering with Cross-modal Feature Graphing
Yuanhe Tian, Chen Su, Junwen Duan +1
Visual question answering (VQA) in medical imaging aims to support clinical diagnosis by automatically interpreting complex imaging data in response to natural language queries. Ex…
Recurrent Visual Feature Extraction and Stereo Attentions for CT Report Generation
Yuanhe Tian, Lei Mao, Yan Song
Generating reports for computed tomography (CT) images is a challenging task, while similar to existing studies for medical image report generation, yet has its unique characterist…
Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis
Yuanhe Tian, Xu Li, Wei Wang +3
Aspect-based sentiment analysis (ABSA) generally requires a deep understanding of the contextual information, including the words associated with the aspect terms and their syntact…
Representation Decomposition for Learning Similarity and Contrastness Across Modalities for Affective Computing
Yuanhe Tian, Pengsen Cheng, Guoqing Jin +2
Multi-modal affective computing aims to automatically recognize and interpret human attitudes from diverse data sources such as images and text, thereby enhancing human-computer in…
Detoxification of Large Language Models through Output-layer Fusion with a Calibration Model
Yuanhe Tian, Mingjie Deng, Guoqing Jin +1
Existing approaches for Large language model (LLM) detoxification generally rely on training on large-scale non-toxic or human-annotated preference data, designing prompts to instr…
DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding Units
Lei Mao, Yuanhe Tian, Yan Song
Genome modeling conventionally treats gene sequence as a language, reflecting its structured motifs and long-range dependencies analogous to linguistic units and organization princ…
Multimodal Conditioned Diffusive Time Series Forecasting
Chen Su, Yuanhe Tian, Yan Song
Diffusion models achieve remarkable success in processing images and text, and have been extended to special domains such as time series forecasting (TSF). Existing diffusion-based…
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences
Yuanhe Tian, Ruyi Gan, Yan Song +2
Recently, the increasing demand for superior medical services has highlighted the discrepancies in the medical infrastructure. With big data, especially texts, forming the foundati…
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks
Yuanhe Tian, Yan Song, Fei Xia
Supertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly importan…
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Yuyang Nie, Yuanhe Tian, Yan Song +2
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the…
Named Entity Recognition for Social Media Texts with Semantic Augmentation
Yuyang Nie, Yuanhe Tian, Xiang Wan +2
Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts, especially user-generated social media content. Sema…