6 papers
MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments
Yin Cai, Zhouhong Gu, Zhaohan Du +5
Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly i…
Towards the Law of Capacity Gap in Distilling Language Models
Chen Zhang, Qiuchi Li, Dawei Song +3
Language model (LM) distillation aims at distilling the knowledge in a large teacher LM to a small student one. As a critical issue facing LM distillation, a superior student often…
AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
Zhouhong Gu, Xiaoxuan Zhu, Yin Cai +12
Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains. However, current frameworks face cr…
PaRT: Enhancing Proactive Social Chatbots with Personalized Real-Time Retrieval
Zihan Niu, Zheyong Xie, Shaosheng Cao +9
Social chatbots have become essential intelligent companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive…
DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?
Zhouhong Gu, Lin Zhang, Xiaoxuan Zhu +8
Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen contex…
MoDification: Mixture of Depths Made Easy
Chen Zhang, Meizhi Zhong, Qimeng Wang +8
Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…