most citedEvent Grounded Criminal Court View Generation with Cooperative (Large) Language Models

5 citations · 5 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI2025

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…

cs.LG2025

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…

cs.CY2025

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…

cs.CL20245 cited

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…

cs.LG2024

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…

cs.LG2024

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…