5 citations · 19 across the 21 of their papers we have counts for
8 papers · 1 filter
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…
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
Wei Song, Zhenya Huang, Cheng Cheng +5
Large language models (LLMs) have demonstrated exceptional performance across a wide range of natural language tasks. However, selecting the optimal LLM to respond to a user query…
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…
TestAgent: An Adaptive and Intelligent Expert for Human Assessment
Junhao Yu, Yan Zhuang, YuXuan Sun +5
Accurately assessing internal human states is key to understanding preferences, offering personalized services, and identifying challenges in real-world applications. Originating f…
CoderAgent: Simulating Student Behavior for Personalized Programming Learning with Large Language Models
Yi Zhan, Qi Liu, Weibo Gao +5
Personalized programming tutoring, such as exercise recommendation, can enhance learners' efficiency, motivation, and outcomes, which is increasingly important in modern digital ed…
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…
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation
Zheng Zhang, Ning Li, Qi Liu +6
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by retrieving relevant document from external knowledge sources. By referencing this external knowledge,…
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…