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cs.CL2023★ 3 cited
Language Models Hallucinate, but May Excel at Fact Verification
Jian Guan, Jesse Dodge, David Wadden +2
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently "hallucinate," resulting in non…
cs.CL2023
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation
Jian Guan, Minlie Huang
Despite the huge progress in myriad generation tasks, pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts with maximization-based decoding algorit…
cs.CL2023
ReDial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training
Jiaxin Wen, Hao Zhou, Jian Guan +1
Pre-training on large-scale open-domain dialogue data can substantially improve the performance of dialogue models. However, the pre-trained dialogue model's ability to utilize lon…