Search Results for author:
Found 5 papers, 2 papers with code
Emergent Abilities in Reduced-Scale Generative Language Models
Large language models can solve new tasks without task-specific fine-tuning.
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data
Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems.
Honey, I Shrunk the Language: Language Model Behavior at Reduced Scale
The majority of recent scaling laws studies focused on high-compute high-parameter count settings, leaving the question of when these abilities begin to emerge largely unanswered.
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
This paper presents a systematic overview and comparison of parameter-efficient fine-tuning methods covering over 40 papers published between February 2019 and February 2023.
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain.
