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
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Wenda Xu, Yilin Tuan, Yujie Lu, Michael Saxon, Lei Li, William Yang Wang
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Zonghan Yang, Xiaoyuan Yi, Peng Li, Yang Liu, Xing Xie
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, Yejin Choi
ContraCLM: Contrastive Learning For Causal Language Model
Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
Atsumoto Ohashi, Ryuichiro Higashinaka
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein