4 papers
DSCD: Large Language Model Detoxification with Self-Constrained Decoding
Ming Dong, Jinkui Zhang, Bolong Zheng +3
Detoxification in large language models (LLMs) remains a significant research challenge. Existing decoding detoxification methods are all based on external constraints, which requi…
FISH-Tuning: Enhancing PEFT Methods with Fisher Information
Kang Xue, Ming Dong, Xinhui Tu +1
The rapid growth in the parameter size of Large Language Models (LLMs) has spurred the development of Parameter-Efficient Fine-Tuning (PEFT) methods to mitigate the substantial com…
Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
Ming Dong, Yujing Chen, Miao Zhang +2
Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approac…
Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection
Ming Dong, Kang Xue, Bolong Zheng +1
Fine-tuning all parameters of Large Language Models (LLMs) is computationally expensive. Parameter-Efficient Fine-Tuning (PEFT) methods address this by selectively fine-tuning spec…