47 citations · 68 across the 8 of their papers we have counts for
5 papers · 1 filter
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents
Ziyang Miao, Qiyu Sun, Jingyuan Wang +4
Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quali…
Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation
Junhao Zhang, Richong Zhang, Fanshuang Kong +3
Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. As input length increases, LLMs increas…
EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
Zhangchi Feng, Dongdong Kuang, Zhongyuan Wang +3
This paper presents EasyRAG, a simple, lightweight, and efficient retrieval-augmented generation framework for automated network operations. Our framework has three advantages. The…
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models
Yaowei Zheng, Richong Zhang, Junhao Zhang +4
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different model…
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Qianben Chen, Richong Zhang, Yaowei Zheng +1
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to…