42 citations · 48 across the 10 of their papers we have counts for
10 papers
World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation
Ang Li, Xin Xu, Bin Liang +4
Reinforcement learning for user-centric agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual comparisons under t…
RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU
Chengyuan Liu, Shihang Wang, Fubang Zhao +5
Information Extraction (IE) and Text Classification (CLS) serve as the fundamental pillars of NLU, with both disciplines relying on analyzing input sequences to categorize outputs…
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
Chengyuan Liu, Yangyang Kang, Shihang Wang +5
The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). Howe…
Evolving Knowledge Distillation with Large Language Models and Active Learning
Chengyuan Liu, Yangyang Kang, Fubang Zhao +4
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, p…
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs
Chengyuan Liu, Fubang Zhao, Lizhi Qing +4
Large Language Models (LLMs) presents significant priority in text understanding and generation. However, LLMs suffer from the risk of generating harmful contents especially while…
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination
Kai Zhang, Yangyang Kang, Fubang Zhao +1
Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, medical assistants hold the…