3 citations · 6 across the 5 of their papers we have counts for
5 papers
Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
Yihang Peng, Peng Jin, Jie Gong +4
Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods being particularly attractive be…
Distributional Discrepancy: A Metric for Unconditional Text Generation
Ping Cai, Xingyuan Chen, Peng Jin +2
The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However,…
Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +3
The autoregressive language model (ALM) trained with maximum likelihood estimation (MLE) is widely used in unconditional text generation. Due to exposure bias, the generated texts…
The Detection of Distributional Discrepancy for Text Generation
Xingyuan Chen, Ping Cai, Peng Jin +4
The text generated by neural language models is not as good as the real text. This means that their distributions are different. Generative Adversarial Nets (GAN) are used to allev…
Adversarial Sub-sequence for Text Generation
Xingyuan Chen, Yanzhe Li, Peng Jin +4
Generative adversarial nets (GAN) has been successfully introduced for generating text to alleviate the exposure bias. However, discriminators in these models only evaluate the ent…