Search Results for author: Baochang Ma

Found 8 papers, 5 papers with code

A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model

1 code implementation17 Apr 2023 Xianghui Sun, Yunjie Ji, Baochang Ma, Xiangang Li

In this study, we undertook experimental comparisons between full-parameter fine-tuning and LoRA-based tuning methods, utilizing LLaMA as the base model.

Instruction Following Language Modelling +1

Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation

1 code implementation16 Apr 2023 Yunjie Ji, Yan Gong, Yong Deng, Yiping Peng, Qiang Niu, Baochang Ma, Xiangang Li

Recently, significant public efforts have been directed towards developing low-cost models with capabilities akin to ChatGPT, thereby fostering the growth of open-source conversational models.

Instruction Following

Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases

1 code implementation26 Mar 2023 Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, Xiangang Li

However current research rarely studies the impact of different amounts of instruction data on model performance, especially in the real-world use cases.


BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

no code implementations SemEval (NAACL) 2022 Yong Deng, Chenxiao Dou, Liangyu Chen, Deqiang Miao, Xianghui Sun, Baochang Ma, Xiangang Li

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media. Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed.

Binary Classification Multi-Label Classification +4

Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

no code implementations18 Mar 2020 Haiyang Xu, Yun Wang, Kun Han, Baochang Ma, Junwen Chen, Xiangang Li

Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary.

Abstractive Text Summarization Document Summarization

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