Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation

arXiv:2404.12596v1 Announce Type: cross
Abstract: Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These models have exhibited the most effective performance in a range of domains within the Natural Language Processing and Generation domains. However, their application in domain-specific tasks, such as paraphrasing, presents significant challenges. The extensive number of parameters makes them difficult to operate on commercial hardware, and they require …

abstract arxiv cs.ai cs.cl cs.lg distillation diverse domains introduction knowledge language language generation language models language processing large language large language models llms natural natural language natural language generation natural language processing nlg performance processing type