3 papers
cs.HC2026
Making Sense of Scams: Understanding Scam Conversations Through Multi-Level Alignment
Zhenyu Mao, Jacky Keung, Xiangyu Li +4
Online scams often unfold gradually through interaction, yet existing detection systems predominantly rely on snapshot-based signals and interruptive warnings, revealing two resear…
cs.CL2026
Adam's Law: Textual Frequency Law on Large Language Models
Hongyuan Adam Lu, Z. L., Victor Wei +5
While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…
cs.AI2025
TableReasoner: Advancing Table Reasoning Framework with Large Language Models
Sishi Xiong, Dakai Wang, Yu Zhao +8
The paper presents our system developed for table question answering (TQA). TQA tasks face challenges due to the characteristics of real-world tabular data, such as large size, inc…