1.9k citations · 2.4k across the 13 of their papers we have counts for
13 papers
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin +3
Low-rank adaptations (LoRA) are often employed to fine-tune large language models (LLMs) for new tasks. This paper investigates LoRA composability for cross-task generalization and…
From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Qian Liu, Fan Zhou, Zhengbao Jiang +2
Fine-tuning language models on tasks with instructions has demonstrated potential in facilitating zero-shot generalization to unseen tasks. In this paper, we introduce a straightfo…
Quaternion-based machine learning on topological quantum systems
Min-Ruei Lin, Wan-Ju Li, Shin-Ming Huang
Topological phase classifications have been intensively studied via machine-learning techniques where different forms of the training data are proposed in order to maximize the inf…
CINO: A Chinese Minority Pre-trained Language Model
Ziqing Yang, Zihang Xu, Yiming Cui +4
Multilingual pre-trained language models have shown impressive performance on cross-lingual tasks. It greatly facilitates the applications of natural language processing on low-res…
Causal Attention for Interpretable and Generalizable Graph Classification
Yongduo Sui, Xiang Wang, Jiancan Wu +3
In graph classification, attention and pooling-based graph neural networks (GNNs) prevail to extract the critical features from the input graph and support the prediction. They mos…
Continual Learning from the Perspective of Compression
Xu He, Min Lin
Connectionist models such as neural networks suffer from catastrophic forgetting. In this work, we study this problem from the perspective of information theory and define forgetti…