104 citations · 164 across the 32 of their papers we have counts for
9 papers · 2 filters
Semi-supervised Fine-tuning for Large Language Models
Junyu Luo, Xiao Luo, Xiusi Chen +3
Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practic…
Self-Updatable Large Language Models by Integrating Context into Model Parameters
Yu Wang, Xinshuang Liu, Xiusi Chen +3
Despite significant advancements in large language models (LLMs), the rapid and frequent integration of small-scale experiences, such as interactions with surrounding objects, rema…
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery
Yu Zhang, Xiusi Chen, Bowen Jin +4
In many scientific fields, large language models (LLMs) have revolutionized the way text and other modalities of data (e.g., molecules and proteins) are handled, achieving superior…
Large Scale Knowledge Washing
Yu Wang, Ruihan Wu, Zexue He +2
Large language models show impressive abilities in memorizing world knowledge, which leads to concerns regarding memorization of private information, toxic or sensitive knowledge,…
IterAlign: Iterative Constitutional Alignment of Large Language Models
Xiusi Chen, Hongzhi Wen, Sreyashi Nag +5
With the rapid development of large language models (LLMs), aligning LLMs with human values and societal norms to ensure their reliability and safety has become crucial. Reinforcem…