7 papers
Thinking Deeper, Not Longer: Depth-Recurrent Transformers for Compositional Generalization
Hung-Hsuan Chen
Standard Transformers have a fixed computational depth, fundamentally limiting their ability to generalize to tasks requiring variable-depth reasoning, such as multi-hop graph trav…
Structure-Preserving Graph Contrastive Learning for Mathematical Information Retrieval
Chun-Hsi Ku, Hung-Hsuan Chen
This paper introduces Variable Substitution as a domain-specific graph augmentation technique for graph contrastive learning (GCL) in the context of searching for mathematical form…
More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
Evan Chen, Run-Jun Zhan, Yan-Bai Lin +1
Large Language Models (LLMs) have revolutionized natural language processing, yet concerns persist regarding their tendency to reflect or amplify social biases. This study introduc…
SCPL: Enhancing Neural Network Training Throughput with Decoupled Local Losses and Model Parallelism
Ming-Yao Ho, Cheng-Kai Wang, You-Teng Lin +1
Adopting large-scale AI models in enterprise information systems is often hindered by high training costs and long development cycles, posing a significant managerial challenge. Th…
GraphFusionSBR: Denoising Multi-Channel Graphs for Session-Based Recommendation
Jia-Xin He, Hung-Hsuan Chen
Session-based recommendation systems must capture implicit user intents from sessions. However, existing models suffer from issues such as item interaction dominance and noisy sess…
Contrastive ECOC: Learning Output Codes for Adversarial Defense
Che-Yu Chou, Hung-Hsuan Chen
Although one-hot encoding is commonly used for multiclass classification, it is not always the most effective encoding mechanism. Error Correcting Output Codes (ECOC) address multi…