5 papers
RevMUX: Data Multiplexing with Reversible Adapters for Efficient LLM Batch Inference
Yige Xu, Xu Guo, Zhiwei Zeng +1
Large language models (LLMs) have brought a great breakthrough to the natural language processing (NLP) community, while leading the challenge of handling concurrent customer queri…
One2Set: Generating Diverse Keyphrases as a Set
Jiacheng Ye, Tao Gui, Yichao Luo +2
Recently, the sequence-to-sequence models have made remarkable progress on the task of keyphrase generation (KG) by concatenating multiple keyphrases in a predefined order as a tar…
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning
Yichao Luo, Yige Xu, Jiacheng Ye +2
Aiming to generate a set of keyphrases, Keyphrase Generation (KG) is a classical task for capturing the central idea from a given document. Based on Seq2Seq models, the previous re…
Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
Yige Xu, Xipeng Qiu, Ligao Zhou +1
Fine-tuning pre-trained language models like BERT has become an effective way in NLP and yields state-of-the-art results on many downstream tasks. Recent studies on adapting BERT t…
How to Fine-Tune BERT for Text Classification?
Chi Sun, Xipeng Qiu, Yige Xu +1
Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Enc…