71 citations · 88 across the 13 of their papers we have counts for
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cs.CL2023
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Cheng Peng, Xi Yang, Aokun Chen +6
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via pr…
cs.CL2023★ 1 cited
On the Impact of Cross-Domain Data on German Language Models
Amin Dada, Aokun Chen, Cheng Peng +12
Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…
cs.CL2023★ 71 cited
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
Cheng Peng, Xi Yang, Kaleb E Smith +4
Objective To develop soft prompt-based learning algorithms for large language models (LLMs), examine the shape of prompts, prompt-tuning using frozen/unfrozen LLMs, transfer learni…