SERVAL: Synergy Learning for Zero-shot Medical Prediction

Khái niệm cốt lõi

The author proposes SERVAL, a synergy learning pipeline that enhances zero-shot medical prediction by leveraging the knowledge of large language models and small vertical models through mutual enhancement.

Tóm tắt

SERVAL introduces a novel approach to unsupervised development of vertical capabilities in both large language models (LLMs) and small models. By utilizing LLMs' zero-shot outcomes as annotations, SERVAL enhances the model's vertical capabilities without the need for manual annotations. The synergy learning process involves iterative training between LLMs and vertical models, resulting in competitive performance across various medical datasets.

Thống kê

"comprehensive experiments show that, without access to any gold labels, SERVAL with the synergy learning of OpenAI GPT-3.5 and a simple model attains fully-supervised competitive performance across ten widely used medical datasets."
"SERVAL consistently surpasses direct LLM predictions and demonstrates the ability to mutually enhance the zero-shot capabilities of both the LLM and a vertical model."
"SERVAL helps the vertical models recover 95% AUC and 92% accuracy in average without access to any gold labels across 10 different diagnosis tasks."
"In extensive experiments, SERVAL emerges as a promising approach for LLM zero-shot prediction and cost-free annotation in medical or other vertical domains."
"The success of SERVAL pipeline depends on the inherent massive knowledge of LLMs."

Trích dẫn

"SERVAL achieves label-free prediction through synergistic training involving an LLM and a vertical model."
"Extensive experiments demonstrate that SERVAL attains fully-supervised competitive performance across various life-threatening disease diagnosis tasks."
"The success of SERVAL pipeline depends on the inherent massive knowledge of LLMs."

Thông tin chi tiết chính được chắt lọc từ

SERVAL

by Jiahuan Yan,... lúc arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01570.pdf

Yêu cầu sâu hơn

How can SERVAL be adapted for applications beyond medical diagnosis

SERVAL can be adapted for applications beyond medical diagnosis by customizing the pipeline to suit different specialized domains. For example, in legal contexts, SERVAL could be used to predict case outcomes based on legal documents and precedents. In financial services, it could assist in risk assessment and fraud detection by leveraging LLMs' zero-shot capabilities. By adjusting the prompts and training data to align with the specific requirements of each domain, SERVAL can be tailored for diverse applications.

What are potential drawbacks or limitations of using unsupervised methods like SERVAL in specialized domains

One potential drawback of using unsupervised methods like SERVAL in specialized domains is the reliance on initial annotations provided by LLMs. If these annotations are inaccurate or biased, they can lead to suboptimal performance of the vertical models trained through SERVAL. Additionally, without human supervision or correction mechanisms in place, there is a risk of propagating errors throughout the iterative process, potentially leading to misleading results or reinforcing incorrect patterns.

How might advancements in large language models impact future developments in unsupervised learning pipelines like SERVAL

Advancements in large language models are likely to have a significant impact on future developments in unsupervised learning pipelines like SERVAL. As LLMs continue to improve their zero-shot capabilities and domain-specific knowledge, they will become more reliable sources of annotations for training vertical models without manual supervision. This progress may enable more sophisticated applications across various industries by reducing the need for labor-intensive annotation processes and accelerating model development timelines. Furthermore, as LLMs evolve to handle increasingly complex tasks with higher accuracy levels, unsupervised learning pipelines like SERVAL may benefit from enhanced performance and efficiency in generating expert-level predictions across specialized domains.