3 citations · 8 across the 10 of their papers we have counts for
11 papers
Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing
Duantengchuan Li, Yingqian Bi, Jinsong Chen +2
Knowledge tracing (KT) aims to predict students' future performance by modeling their evolving knowledge states from historical interactions. Existing KT methods usually treat the…
MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing
Yuhao Jia, Duantengchuan Li, Jinsong Chen +4
The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core idea is to extract the coll…
Documents Are People and Words Are Items: A Psychometric Approach to Textual Data with Contextual Embeddings
Jinsong Chen
This research introduces a novel psychometric method for analyzing textual data using large language models. By leveraging contextual embeddings to create contextual scores, we tra…
Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoring
Shiting Chen, Zijian Zhao, Jinsong Chen
Large Language Models (LLMs) hold significant promise for mathematics education, yet they often struggle with complex mathematical reasoning. While Retrieval-Augmented Generation (…
DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification
Chenyang Li, Jinsong Chen, John E. Hopcroft +1
Neighborhood-aware tokenized graph Transformers have recently shown great potential for node classification tasks. Despite their effectiveness, our in-depth analysis of neighborhoo…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
Rethinking Tokenized Graph Transformers for Node Classification
Jinsong Chen, Chenyang Li, GaiChao Li +2
Node tokenized graph Transformers (GTs) have shown promising performance in node classification. The generation of token sequences is the key module in existing tokenized GTs which…
Mixture of Message Passing Experts with Routing Entropy Regularization for Node Classification
Xuanze Chen, Jiajun Zhou, Yadong Li +3
Graph neural networks (GNNs) have achieved significant progress in graph-based learning tasks, yet their performance often deteriorates when facing heterophilous structures where c…
OASIS: Open Agent Social Interaction Simulations with One Million Agents
Ziyi Yang, Zaibin Zhang, Zirui Zheng +20
There has been a growing interest in enhancing rule-based agent-based models (ABMs) for social media platforms (i.e., X, Reddit) with more realistic large language model (LLM) agen…
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
Jinsong Chen, Siyu Jiang, Kun He
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the…
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
Jinsong Chen, Hanpeng Liu, John E. Hopcroft +1
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for co…