5 citations · 13 across the 14 of their papers we have counts for
17 papers
SelectTTRL: Representation-Guided Data Selection for Efficient Test-Time Reinforcement Learning
Yi Li, Linan Yue, Weibo Gao
Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from multiple sampled responses. Ho…
From Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation
Zijian Chen, Zheng Zhang, Miao Jia +3
Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine-grained learning behaviors,…
DuoOPD: Learning from Joint Teacher-Student Outcomes for Multi-Task On-Policy Distillation
Ao Yu, Weibo Gao, Heng Zhou +6
On-policy distillation (OPD) trains a student on its own responses with token-level feedback from a stronger teacher, yet the teacher can fail on questions the student already answ…
Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call
Weibo Gao, Qi Liu, Linan Yue +6
Large-scale learner-task interaction data are crucial for intelligent educational systems but are costly to collect and constrained by privacy and learner engagement. Learner simul…
Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
Linan Yue, Yichao Du, Yizhi Wang +8
Recently, Large Reasoning Models (LRMs) have gradually become a research hotspot due to their outstanding performance in handling complex tasks. Among them, DeepSeek R1 has garnere…
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor
Weibo Gao, Qi Liu, Rui Li +5
Programming Knowledge Tracking (PKT) aims to dynamically diagnose learners' mastery levels of programming knowledge based on their coding activities, facilitating more effective an…
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
Rui Lv, Zaixi Zhang, Kai Zhang +6
Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in t…
Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
Weibo Gao, Qi Liu, Linan Yue +5
Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy…
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Weibo Gao, Qi Liu, Linan Yue +4
Learners sharing similar implicit cognitive states often display comparable observable problem-solving performances. Leveraging collaborative connections among such similar learner…
Towards Few-shot Self-explaining Graph Neural Networks
Jingyu Peng, Qi Liu, Linan Yue +3
Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as…
Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models
Ye Liu, Kai Zhang, Aoran Gan +4
Few-Shot Relation Extraction (FSRE), a subtask of Relation Extraction (RE) that utilizes limited training instances, appeals to more researchers in Natural Language Processing (NLP…
Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models
Linan Yue, Qi Liu, Lili Zhao +3
With the development of legal intelligence, Criminal Court View Generation has attracted much attention as a crucial task of legal intelligence, which aims to generate concise and…
Cooperative Classification and Rationalization for Graph Generalization
Linan Yue, Qi Liu, Ye Liu +3
Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) d…
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
Linan Yue, Qi Liu, Yichao Du +3
The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to suppo…
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
Yichao Du, Zhirui Zhang, Linan Yue +5
To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recogn…
Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives
Weibo Gao, Qi Liu, Hao Wang +7
Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance withi…
FedJudge: Federated Legal Large Language Model
Linan Yue, Qi Liu, Yichao Du +3
Large Language Models (LLMs) have gained prominence in the field of Legal Intelligence, offering potential applications in assisting legal professionals and laymen. However, the ce…