반응형 자연어처리572 [2023-05-06] 오늘의 자연어처리 2x Faster Language Model Pre-training via Masked Structural Growth Acceleration of large language model pre-training is a critical issue in present NLP research. In this paper, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one. There are two main research problems related to progressive growth: growth schedule and growth operator. For.. 2023. 5. 6. [2023-05-05] 오늘의 자연어처리 Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token. However, recent studies have established that MoE models are inherently parameter-ineffici.. 2023. 5. 5. [2023-05-04] 오늘의 자연어처리 Clinical Note Generation from Doctor-Patient Conversations using Large Language Models: Insights from MEDIQA-Chat This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses.. 2023. 5. 4. [2023-05-04] 오늘의 자연어처리 A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge Recently there have been efforts to introduce new benchmark tasks for spoken language understanding (SLU), like semantic parsing. In this paper, we describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge.. 2023. 5. 4. 이전 1 ··· 62 63 64 65 66 67 68 ··· 143 다음 반응형