4 papers
Breaking the Martingale Curse: Multi-Agent Debate via Asymmetric Cognitive Potential Energy
Yuhan Liu, Juntian Zhang, Yichen Wu +4
Multi-Agent Debate (MAD) has emerged as a promising paradigm for enhancing large language model reasoning. However, recent work reveals a limitation:standard MAD cannot improve bel…
AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications
Honglin Mu, Jinghao Liu, Kaiyang Wan +4
Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content moderation. However, our research identi…
From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Jinghui Zhang, Kaiyang Wan, Longwei Xu +3
Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for cris…
Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching
Mingzhe Li, Jing Xiang, Qishen Zhang +2
Knowledge distillation typically involves transferring knowledge from a Large Language Model (LLM) to a Smaller Language Model (SLM). However, in tasks such as text matching, fine-…