Evaluating Safety and Alignment of Large Language Models for Medicine

Khái niệm cốt lõi

The author highlights the importance of evaluating the safety and alignment of medical large language models (LLMs) due to potential risks in healthcare settings. They propose a methodology to define, assess, and mitigate harmful outputs from medical LLMs.

Tóm tắt

Large language models (LLMs) in medicine pose both benefits and risks, necessitating an evaluation of their safety and alignment. The authors introduce a dataset of harmful medical prompts based on ethical principles to assess LLM responses. Results show that newer aligned LLMs exhibit lower harmfulness scores compared to non-aligned models, emphasizing the need for ongoing research in this area.

Thống kê

The med-harm dataset consists of 1,742 harmful medical prompts.
Meditron-70b consistently shows low harmfulness scores across all datasets.
General-knowledge LLMs occasionally output responses with high harmfulness scores.

Trích dẫn

"An LLM’s refusal to respond to a harmful prompt indicates that it did not provide harmful information."
"Medical LLMs are at risk of outputting more harmful outputs than general-knowledge LLMs."

Thông tin chi tiết chính được chắt lọc từ

Towards Safe and Aligned Large Language Models for Medicine

by Tessa Han,Ao... lúc arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03744.pdf

Yêu cầu sâu hơn

How can the machine learning community ensure continuous improvement in the safety and alignment of medical large language models?

Continuous improvement in the safety and alignment of medical large language models (LLMs) can be ensured through several key strategies:

Robust Evaluation Frameworks: Developing comprehensive evaluation frameworks specifically tailored for medical LLMs to assess their safety and alignment. This includes creating diverse datasets of harmful prompts, refining metrics for measuring harmfulness scores, and incorporating real-world scenarios into evaluations.

Iterative Training Processes: Implementing iterative training processes that involve ongoing feedback loops from domain experts, healthcare professionals, ethicists, and end-users to continuously refine model behavior based on real-world applications.

Fine-tuning Techniques: Utilizing fine-tuning techniques on medical LLMs with safety demonstrations to enhance their ability to recognize and avoid harmful outputs. Fine-tuning allows for targeted adjustments to improve model performance while maintaining alignment with ethical guidelines.

Collaboration with Healthcare Professionals: Collaborating closely with healthcare professionals to understand specific use cases, ethical considerations, regulatory requirements, and potential risks associated with deploying LLMs in clinical settings.

Ethical Review Boards: Establishing dedicated ethical review boards or committees within organizations developing medical LLMs to provide oversight on model development, deployment decisions, data handling practices, and adherence to ethical standards.

Transparency and Accountability: Promoting transparency by documenting model architectures, training data sources, decision-making processes within the models, as well as ensuring accountability mechanisms are in place for addressing any unintended consequences or biases.

By implementing these strategies collectively within the machine learning community's efforts towards advancing safe and aligned medical LLMs will lead to continuous improvements in their overall efficacy and reliability.

What ethical considerations should be prioritized when developing mitigation strategies for harmful outputs from medical LLMs?

When developing mitigation strategies for harmful outputs from medical Large Language Models (LLMs), several critical ethical considerations must be prioritized:

Patient Safety & Well-being: The primary focus should always be on protecting patient safety and well-being by ensuring that any information provided by the LLM does not pose a risk or harm to individuals seeking healthcare advice or treatment.

Privacy & Confidentiality: Mitigation strategies must prioritize maintaining patient privacy rights by safeguarding confidential health information shared during interactions with the LLM while adhering strictly to legal regulations such as HIPAA compliance.

Informed Consent & Autonomy : Respecting patients' autonomy by providing transparent information about interacting with an AI system like an LLM so that users can make informed decisions about engaging with it voluntarily without coercion or manipulation.

4 .Bias Detection & Mitigation: Prioritizing bias detection mechanisms throughout all stages of development - from data collection through deployment - is crucial because biased algorithms could perpetuate disparities in healthcare outcomes among different demographic groups
5 .Accountability & Transparency: Ensuring clear accountability structures are established where responsibility lies if harm occurs due incorrect output generated by an ML algorithm
6 .Continual Monitoring & Feedback: Regularly monitoring system performance post-deployment is essential alongwith gathering feedback from users , clinicians etc which helps identify issues early
Prioritizing these ethics ensures responsible development of mitigation strategies that uphold patient trust , protect individual rights , promote fairness,and ultimately contribute positively towards improving healthcare delivery using AI technologies.

How might advancements in aligned behavior impact the future integration of large language models in healthcare?

Advancements in aligned behavior have significant implications for integrating large language models (LLMs) into healthcare:
1 .Enhanced Patient Care:
Aligned behavior ensures that responses generated by Medical LLMS align closely human intentions,prefrences values thus enhancing quality care delivery .
2 .Improved Clinical Decision-Making:
With better-aligned behaviors,Large Language Models can assist clinicians making more accurate diagnoses,treatment plans,recommendations leading improved clinical outcomes
3 .Reduced Errors& Risks:
Alignement reduces chances errors,misinformation being disseminated thereby minimizing risks posed patients receiving inaccurate advice/treatments
4 .Efficiency Gains:
Aligned Medical LLMS help streamline administrative tasks,such as documentation coding freeing up time physicians focus direct patient care
5 .Regulatory Compliance:
Adherence strict Ethical Guidelines Regulatory Standards facilitated Aligned Behavior mitigates concerns around liability,data privacy security issues
Overall,the advancement Aligned Behavior Large Language Models holds promise revolutionize Healthcare Industry offering innovative solutions challenges faced today delivering superior quality care efficient manner