57 citations · 156 across the 13 of their papers we have counts for
9 papers · 1 filter
AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
Chi-Min Chan, Jianxuan Yu, Weize Chen +6
The rapid advancement of large language models (LLMs) has led to the rise of LLM-based agents. Recent research shows that multi-agent systems (MAS), where each agent plays a specif…
Importance Weighting Can Help Large Language Models Self-Improve
Chunyang Jiang, Chi-min Chan, Wei Xue +2
Large language models (LLMs) have shown remarkable capability in numerous tasks and applications. However, fine-tuning LLMs using high-quality datasets under external supervision r…
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
Chi-Min Chan, Chunpu Xu, Ruibin Yuan +4
Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pr…
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su +5
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have…
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Weize Chen, Yusheng Su, Jingwei Zuo +13
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-…
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng +9
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize…
Plug-and-Play Document Modules for Pre-trained Models
Chaojun Xiao, Zhengyan Zhang, Xu Han +7
Large-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in t…
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ning Ding, Yujia Qin, Guang Yang +17
Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining sepa…
On Transferability of Prompt Tuning for Natural Language Processing
Yusheng Su, Xiaozhi Wang, Yujia Qin +10
Prompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-paramet…