48 citations · 57 across the 6 of their papers we have counts for
6 papers
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
Xiao Wang, Tianze Chen, Xianjun Yang +3
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on…
Counting Objects in a Robotic Hand
Francis Tsow, Tianze Chen, Yu Sun
A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping. The count plays an important role in determining the robot's next move and…
Orthogonal Subspace Learning for Language Model Continual Learning
Xiao Wang, Tianze Chen, Qiming Ge +6
Benefiting from massive corpora and advanced hardware, large language models (LLMs) exhibit remarkable capabilities in language understanding and generation. However, their perform…
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
Xiao Wang, Yuansen Zhang, Tianze Chen +9
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of th…
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Xiao Wang, Weikang Zhou, Can Zu +11
Large language models have unlocked strong multi-task capabilities from reading instructive prompts. However, recent studies have shown that existing large models still have diffic…
Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policy
Adheesh Shenoy, Tianze Chen, Yu Sun
Transferring multiple objects between bins is a common task for many applications. In robotics, a standard approach is to pick up one object and transfer it at a time. However, gra…