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
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
Zhecheng Sheng, Raymond Finzel, Michael Lucke, Sheena Dufresne, Maria Gini, Serguei Pakhomov
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models
Adian Liusie, Potsawee Manakul, Mark J. F. Gales
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models
Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang, Zhengrui Ma, Yan Zhou, Langlin Huang, Mengyu Bu, Shangtong Gui, Yunji Chen, Xilin Chen, Yang Feng
Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis
Jun-Min Lee, Tae-Bin Ha
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Sameer Jain, Vaishakh Keshava, Swarnashree Mysore Sathyendra, Patrick Fernandes, Pengfei Liu, Graham Neubig, Chunting Zhou
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shentao Yang, Shujian Zhang, Congying Xia, Yihao Feng, Caiming Xiong, Mingyuan Zhou