Search Results for author:
Found 6 papers, 3 papers with code
Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning
The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress.
Counting Objects in a Robotic Hand
A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping.
Orthogonal Subspace Learning for Language Model Continual Learning
In this paper, we propose orthogonal low-rank adaptation (O-LoRA), a simple and efficient approach for continual learning in language models, effectively mitigating catastrophic forgetting while learning new tasks.
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
In this paper, we introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Large language models have unlocked strong multi-task capabilities from reading instructive prompts.
Ranked #2 on
Zero-shot Named Entity Recognition (NER)
on CrossNER
(using extra training data)
Multi-Object Grasping -- Estimating the Number of Objects in a Robotic Grasp
To do so, a robot needs to grasp within a pile, sense the number of objects in the grasp before lifting, and predict the number of objects that will remain in the grasp after lifting.
