16 citations · 38 across the 13 of their papers we have counts for
13 papers
Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation
Tingjia Shen, Hao Wang, Chuan Qin +5
Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP), primarily focused on extracting answers from unstructured textual data. With…
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
Xiaoshan Yu, Chuan Qin, Qi Zhang +4
The rapid development of online recruitment platforms has created unprecedented opportunities for job seekers while concurrently posing the significant challenge of quickly and acc…
Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
Chen Yang, Sunhao Dai, Yupeng Hou +4
Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However…
Generative Organizational Behavior Simulation using Large Language Model based Autonomous Agents: A Holacracy Perspective
Chen Zhu, Yihang Cheng, Jingshuai Zhang +3
In this paper, we present the technical details and periodic findings of our project, CareerAgent, which aims to build a generative simulation framework for a Holacracy organizatio…
Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework
Xiao Han, Chen Zhu, Xiangyu Zhao +1
Visual geo-localization demands in-depth knowledge and advanced reasoning skills to associate images with precise real-world geographic locations. Existing image database retrieval…
Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
Xiao Han, Chen Zhu, Xiao Hu +3
Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment…
RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes
Xiaoshan Yu, Chuan Qin, Dazhong Shen +4
In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while t…
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
Hongzhan Lin, Ang Lv, Yuhan Chen +4
Many studies have revealed that large language models (LLMs) exhibit uneven awareness of different contextual positions. Their limited context awareness can lead to overlooking cri…
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking
Xi Chen, Chuan Qin, Chuyu Fang +5
In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills a…
Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach
Feihu Jiang, Chuan Qin, Jingshuai Zhang +6
In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured info…
KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +4
In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that de…
ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence Awareness
Yunfei Zhang, Chuan Qin, Dazhong Shen +4
During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning…
Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation
Wenxuan Zhang, Hongzhi Liu, Yingpeng Du +4
Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks,…