Efficient Test-Time Adaptation of Large Language Models for Improved Medical Reasoning

Core Concepts

MedAdapter, a unified post-hoc adapter, effectively adapts both white-box and black-box large language models for improved performance on biomedical reasoning tasks without requiring extensive computational resources or sharing sensitive data.

Abstract

The paper introduces MedAdapter, a unified post-hoc adapter designed to facilitate the efficient test-time adaptation of large language models (LLMs) towards medical reasoning applications.
Key highlights:

Adapting LLMs to the biomedical domain remains challenging due to their immense size and corporate privacy concerns.
MedAdapter fine-tunes a small BERT-sized adapter (110M parameters) to rank candidate solutions generated by LLMs, effectively adapting the original model without requiring extensive computational resources or sharing data with third parties.
For white-box LLMs, MedAdapter achieves 99.35% of supervised fine-tuning performance using only 14.75% of the GPU memory.
For black-box LLMs, MedAdapter achieves comparable or even superior performance compared to fine-tuning via APIs, at only 15.59% of the cost and without the need to share private data.
When combined with train-time adaptation, MedAdapter outperforms either train-time or test-time adaptation alone, demonstrating its utility as a flexible and complementary solution.

Stats

Supervised fine-tuning of a 7B-parameter LLaMA-2 model requires 78.65 GiB of GPU memory.
MedAdapter, with a 110M-parameter adapter, requires only 11.60 GiB of GPU memory for training.
Supervised fine-tuning of GPT-3.5-turbo via the Microsoft Azure OpenAI API costs $71.18 for training and $10.88 for inference.
MedAdapter costs $7.67 for training and $10.40 for inference, a 15.59% reduction in total cost.

Quotes

"Faced with the challenges of balancing model performance, computational resources, and data privacy, MedAdapter provides an efficient, privacy-preserving, cost-effective, and transparent solution for adapting LLMs to the biomedical domain."
"Experiments demonstrate that MedAdapter effectively adapts both white-box and black-box LLMs for medical reasoning, achieving average performance improvements of 25.48% and 11.31%, respectively."

Key Insights Distilled From

MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning

by Wenqi Shi,Ra... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03000.pdf

Deeper Inquiries

How can MedAdapter be extended to handle on-device inference of sensitive medical data without relying on third-party APIs?

To enable on-device inference of sensitive medical data without depending on third-party APIs, MedAdapter can be extended by implementing a secure and privacy-preserving local inference mechanism. This can involve deploying the adapted model directly on the device where the data resides, ensuring that all computations and predictions are made locally without the need to transmit any data externally. By incorporating encryption techniques and secure computation protocols, MedAdapter can ensure that sensitive information remains protected during the inference process. Additionally, implementing strict access controls and authentication mechanisms can further enhance the security of on-device inference, allowing only authorized users to access and utilize the adapted model for medical data analysis.

What are the potential limitations of MedAdapter's reliance on access to target domain label information during the adapter fine-tuning process?

While MedAdapter's reliance on target domain label information during the adapter fine-tuning process is essential for training the adapter to score candidate solutions accurately, there are potential limitations to consider. One limitation is the requirement for a sufficient amount of labeled data in the target domain to effectively train the adapter. In scenarios where labeled data is scarce or difficult to obtain, the performance of MedAdapter may be limited by the availability of high-quality labeled samples. Additionally, the quality and accuracy of the target domain labels can impact the adaptability and generalization of the adapter to unseen data. Inaccurate or noisy labels can lead to suboptimal performance and hinder the effectiveness of MedAdapter in real-world applications. Furthermore, the need for manual annotation of target domain labels can be time-consuming and resource-intensive, posing challenges in scaling up the adaptation process for large datasets.

Could MedAdapter's approach of leveraging candidate solutions generated by LLMs be applied to other domains beyond medical reasoning, such as scientific computing or legal analysis?

Yes, MedAdapter's approach of utilizing candidate solutions generated by LLMs can be extended to various domains beyond medical reasoning, including scientific computing and legal analysis. In scientific computing, MedAdapter can be adapted to assist researchers in generating hypotheses, analyzing experimental results, and interpreting complex scientific data. By fine-tuning an adapter to rank candidate solutions based on scientific questions or problems, MedAdapter can enhance the efficiency and accuracy of scientific reasoning tasks. Similarly, in legal analysis, MedAdapter can be utilized to evaluate legal documents, conduct case law research, and provide insights into legal queries. By training the adapter on legal domain-specific data and leveraging the generated solutions from LLMs, MedAdapter can support legal professionals in decision-making processes and legal research. Overall, the adaptable nature of MedAdapter makes it a versatile tool that can be tailored to various domains to improve reasoning and decision-making tasks.