Language Generation
Language generation research focuses on creating systems that produce human-quality text, addressing challenges like factual accuracy, style control, and bias mitigation. Current efforts concentrate on improving large language models (LLMs) through techniques such as fine-tuning with various loss functions, efficient parameter-efficient fine-tuning methods, and integrating external knowledge sources. This field is crucial for advancing natural language processing and has significant implications for applications ranging from automated report generation to improved human-computer interaction.
Papers
Efficient Large Language Models: A Survey
Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, Mi Zhang
Measuring Misogyny in Natural Language Generation: Preliminary Results from a Case Study on two Reddit Communities
Aaron J. Snoswell, Lucinda Nelson, Hao Xue, Flora D. Salim, Nicolas Suzor, Jean Burgess