2 citations · 3 across the 3 of their papers we have counts for
4 papers
Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Xiao Han, Zimo Zhao, Wanyu Wang +4
Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially when retraining…
Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models
Dayan Pan, Zhaoyang Fu, Jingyuan Wang +3
Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specifi…
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training
Nan He, Weichen Xiong, Hanwen Liu +6
The effectiveness of large language models (LLMs) is often hindered by duplicated data in their extensive pre-training datasets. Current approaches primarily focus on detecting and…
Zero-shot Cross-lingual Transfer without Parallel Corpus
Yuyang Zhang, Xiaofeng Han, Baojun Wang
Recently, although pre-trained language models have achieved great success on multilingual NLP (Natural Language Processing) tasks, the lack of training data on many tasks in low-r…