4 citations · 4 across the 6 of their papers we have counts for
7 papers
SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models
Binxian Su, Haoye Lou, Shucheng Zhu +4
Large language models (LLMs) are being increasingly used in urban planning, but since gendered space theory highlights how gender hierarchies are embedded in spatial organization,…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens
Weiyao Luo, Suncong Zheng, Heming Xia +5
Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a…
Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT
Shucheng Zhu, Weikang Wang, Ying Liu
While nationality is a pivotal demographic element that enhances the performance of language models, it has received far less scrutiny regarding inherent biases. This study investi…
Multilingual Sentence Transformer as A Multilingual Word Aligner
Weikang Wang, Guanhua Chen, Hanqing Wang +2
Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. I…
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Weikang Wang, Jiajun Zhang, Qian Li +2
Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we f…
Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Weikang Wang, Jiajun Zhang, Qian Li +3
Clarifying user needs is essential for existing task-oriented dialogue systems. However, in real-world applications, developers can never guarantee that all possible user demands a…